CDC Daily by State
davegoblue
2022-09-07
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v004, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions
that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download
on the CDC
website
- CDC hospital data are available for download on the healthdata.gov
website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 0.5.2## Warning: package 'ggplot2' was built under R version 4.2.2## Warning: package 'tidyr' was built under R version 4.2.2## Warning: package 'readr' was built under R version 4.2.2## Warning: package 'purrr' was built under R version 4.2.2## Warning: package 'dplyr' was built under R version 4.2.2## Warning: package 'stringr' was built under R version 4.2.2## Warning: package 'forcats' was built under R version 4.2.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(geofacet)
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download
data
- renMapper - mapping file for renaming of variables in the raw data
file
- selfListMapper - mapping file for transformations by variable
type
- fullListMapper - mapping file for transformations across variable
types
- lstComboMapper - mapping file for elements to be combined by data
type (most common is to combine NYC and NYS data to NY if the file
provides them separately)
- uqMapper - mapping file for fields that should combine to be unique
keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common
is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control
total checks
- checkControlAggMapper - mapping file for numeric variables for
control total checks
- checkSimilarityMapper - mapping file for similarity checks to
perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total
checks to perform
- perCapMapper - named vector that drives conversion from original
field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example Code Processing
The function is run to download and process the latest CDC case, hospitalization, and death data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220907.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220907.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220907.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220805")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220805")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220805")$dfRaw$vax
)
cdc_daily_220907 <- readRunCDCDaily(thruLabel="Sep 05, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 57480 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-07-31 new_deaths 116 23 93 1.33812950
## 2 2022-07-30 new_deaths 130 34 96 1.17073171
## 3 2022-07-23 new_deaths 158 109 49 0.36704120
## 4 2022-07-24 new_deaths 170 126 44 0.29729730
## 5 2022-08-01 new_deaths 433 347 86 0.22051282
## 6 2022-07-28 new_deaths 518 434 84 0.17647059
## 7 2022-07-16 new_deaths 151 127 24 0.17266187
## 8 2022-07-29 new_deaths 639 543 96 0.16243655
## 9 2022-07-25 new_deaths 306 265 41 0.14360771
## 10 2022-08-03 new_deaths 716 632 84 0.12462908
## 11 2022-08-02 new_deaths 715 632 83 0.12323682
## 12 2022-07-27 new_deaths 703 634 69 0.10321616
## 13 2022-07-10 new_deaths 114 103 11 0.10138249
## 14 2022-07-22 new_deaths 628 580 48 0.07947020
## 15 2022-06-18 new_deaths 105 97 8 0.07920792
## 16 2022-07-26 new_deaths 643 596 47 0.07586764
## 17 2022-07-04 new_deaths 138 128 10 0.07518797
## 18 2022-07-18 new_deaths 361 337 24 0.06876791
## 19 2022-07-21 new_deaths 500 471 29 0.05973223
## 20 2022-07-09 new_deaths 110 104 6 0.05607477
## 21 2022-07-30 new_cases 38338 32823 5515 0.15500063
## 22 2022-07-31 new_cases 39803 35276 4527 0.12059298
## 23 2022-08-01 new_cases 126242 133013 6771 0.05223429
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC tot_deaths 11352019 11325521 26498 0.002336938
## 2 KY tot_deaths 6954858 6939424 15434 0.002221633
## 3 NC new_deaths 26101 25692 409 0.015793640
## 4 KY new_deaths 16647 16438 209 0.012634124
## 5 FL new_deaths 78609 77823 786 0.010049095
## 6 AL new_deaths 20081 19974 107 0.005342654
## 7 SC new_deaths 18211 18192 19 0.001043870
## 8 SC new_cases 1626423 1605165 21258 0.013156380
## 9 KY new_cases 1489715 1479668 10047 0.006767062
## 10 NC new_cases 3026839 3022204 4635 0.001532474
##
##
##
## Raw file for cdcDaily:
## Rows: 57,480
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-09-01, 2021-0…
## $ state <chr> "KS", "ND", "AS", "ND", "IN", "FL", "TN", "PR", "PW", "…
## $ tot_cases <dbl> 297229, 163565, 11, 118491, 668765, 3510205, 64885, 173…
## $ conf_cases <dbl> 241035, 135705, NA, 107475, NA, NA, 64371, 144788, NA, …
## $ prob_cases <dbl> 56194, 27860, NA, 11016, NA, NA, 514, 29179, NA, NA, NA…
## $ new_cases <dbl> 0, 589, 0, 536, 487, 9979, 1816, 667, 0, 317, 0, 28, 8,…
## $ pnew_case <dbl> 0, 220, 0, 66, 0, 2709, 30, 274, 0, 0, 0, 5, 0, 46, 70,…
## $ tot_deaths <dbl> 4851, 1907, 0, 1562, 12710, 56036, 749, 2911, 0, 561, 0…
## $ conf_death <dbl> NA, NA, NA, NA, 12315, NA, 722, 2482, NA, NA, NA, 1601,…
## $ prob_death <dbl> NA, NA, NA, NA, 395, NA, 27, 429, NA, NA, NA, 366, NA, …
## $ new_deaths <dbl> 0, 9, 0, 1, 7, 294, 8, 8, 0, 12, 0, 0, 0, 5, 0, 4, 0, 0…
## $ pnew_death <dbl> 0, 0, 0, 0, 2, 26, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases <chr> "Agree", "Agree", NA, "Agree", "Not agree", "Not agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Agree", "Not agre…## Rows: 49367 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-07-25 hosp_ped 3964 4594 630 0.1472307
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 ND inp 122358 122070 288 0.002356522
## 2 NH hosp_ped 1127 1167 40 0.034873583
## 3 KS hosp_ped 4891 4725 166 0.034525790
## 4 ME hosp_ped 2387 2338 49 0.020740741
## 5 KY hosp_ped 20285 20665 380 0.018559219
## 6 WV hosp_ped 5686 5753 67 0.011714311
## 7 VA hosp_ped 18388 18192 196 0.010716238
## 8 TN hosp_ped 22215 22423 208 0.009319414
## 9 NM hosp_ped 8054 8114 60 0.007422068
## 10 SC hosp_ped 9035 9092 57 0.006288961
## 11 DE hosp_ped 5277 5310 33 0.006234061
## 12 NJ hosp_ped 19499 19618 119 0.006084311
## 13 UT hosp_ped 10271 10210 61 0.005956740
## 14 MS hosp_ped 11803 11854 51 0.004311620
## 15 AL hosp_ped 20947 21025 78 0.003716764
## 16 VT hosp_ped 540 542 2 0.003696858
## 17 WY hosp_ped 859 856 3 0.003498542
## 18 MA hosp_ped 12619 12657 38 0.003006805
## 19 NC hosp_ped 30541 30453 88 0.002885530
## 20 PR hosp_ped 23021 22959 62 0.002696825
## 21 IL hosp_ped 44084 44202 118 0.002673131
## 22 AK hosp_ped 2664 2657 7 0.002631084
## 23 MO hosp_ped 39841 39939 98 0.002456756
## 24 PA hosp_ped 55078 55211 133 0.002411845
## 25 AR hosp_ped 12767 12747 20 0.001567767
## 26 CO hosp_ped 22421 22387 34 0.001517586
## 27 OH hosp_ped 91382 91261 121 0.001324989
## 28 AZ hosp_ped 27563 27532 31 0.001125329
## 29 MD hosp_ped 17240 17221 19 0.001102696
## 30 ND hosp_adult 115920 115630 290 0.002504859
##
##
##
## Raw file for cdcHosp:
## Rows: 49,367
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Rows: 36184 Columns: 96
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Distributed_Novavax Administered_Novavax Series_Complete_Novavax
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,184
## Columns: 96
## $ date <date> 2022-08-31, 2022-08-31, 2022-0…
## $ MMWR_week <dbl> 35, 35, 35, 35, 35, 35, 35, 35,…
## $ state <chr> "PW", "SD", "MA", "HI", "RI", "…
## $ Distributed <dbl> 47090, 2141765, 18793570, 38391…
## $ Distributed_Janssen <dbl> 3800, 92800, 626200, 124700, 90…
## $ Distributed_Moderna <dbl> 30000, 847500, 7168380, 1461820…
## $ Distributed_Pfizer <dbl> 13290, 1199665, 10993590, 22498…
## $ Distributed_Novavax <dbl> 0, 1800, 5400, 2800, 3200, 200,…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 218698, 242101, 272667, 271153,…
## $ Distributed_Per_100k_5Plus <dbl> 231139, 260083, 287577, 288518,…
## $ Distributed_Per_100k_12Plus <dbl> 252561, 290172, 312354, 316997,…
## $ Distributed_Per_100k_18Plus <dbl> 283966, 320836, 339252, 344011,…
## $ Distributed_Per_100k_65Plus <dbl> 2363960, 1410250, 1607210, 1430…
## $ vxa <dbl> 49416, 1511407, 15773792, 31561…
## $ Administered_5Plus <dbl> 49373, 1507671, 15687231, 31448…
## $ Administered_12Plus <dbl> 46683, 1451767, 15028043, 30184…
## $ Administered_18Plus <dbl> 43018, 1359766, 14038745, 28178…
## $ Administered_65Plus <dbl> 5346, 453691, 3769576, 842523, …
## $ Administered_Janssen <dbl> 2357, 42334, 407539, 71355, 664…
## $ Administered_Moderna <dbl> 37794, 586034, 6193704, 1157104…
## $ Administered_Pfizer <dbl> 9098, 882891, 9171774, 1927032,…
## $ Administered_Novavax <dbl> 0, 0, 295, 10, 219, 1, 45, 25, …
## $ Administered_Unk_Manuf <dbl> 167, 148, 480, 697, 2259, 9, 25…
## $ Admin_Per_100k <dbl> 229500, 170846, 228854, 222915,…
## $ Admin_Per_100k_5Plus <dbl> 242345, 183083, 240044, 236338,…
## $ Admin_Per_100k_12Plus <dbl> 250378, 196689, 249770, 249232,…
## $ Admin_Per_100k_18Plus <dbl> 259410, 203693, 253421, 252497,…
## $ Admin_Per_100k_65Plus <dbl> 268373, 298734, 322370, 313850,…
## $ Recip_Administered <dbl> 49797, 1533994, 15858274, 31873…
## $ Administered_Dose1_Recip <dbl> 20575, 700878, 6982383, 1266721…
## $ Administered_Dose1_Pop_Pct <dbl> 95.0, 79.2, 95.0, 89.5, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus <dbl> 20547, 698199, 6928829, 1259039…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 95.0, 84.8, 95.0, 94.6, 95.0, 9…
## $ Administered_Dose1_Recip_12Plus <dbl> 19119, 668658, 6593289, 1196906…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 90.6, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_18Plus <dbl> 17584, 622099, 6127404, 1107067…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 93.2, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_65Plus <dbl> 1876, 182192, 1462735, 275645, …
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.2, 95.0, 95.0, 95.0, 95.0, 8…
## $ vxc <dbl> 18338, 563276, 5570460, 1128707…
## $ vxcpoppct <dbl> 85.2, 63.7, 80.8, 79.7, 84.9, 8…
## $ Series_Complete_5Plus <dbl> 18330, 563050, 5551263, 1126786…
## $ Series_Complete_5PlusPop_Pct <dbl> 90.0, 68.4, 84.9, 84.7, 89.4, 9…
## $ Series_Complete_12Plus <dbl> 17241, 539812, 5278994, 1071938…
## $ Series_Complete_12PlusPop_Pct <dbl> 92.5, 73.1, 87.7, 88.5, 92.3, 9…
## $ vxcgte18 <dbl> 15791, 503622, 4896487, 990569,…
## $ vxcgte18pct <dbl> 95.0, 75.4, 88.4, 88.8, 93.0, 9…
## $ vxcgte65 <dbl> 1811, 151094, 1165453, 252765, …
## $ vxcgte65pct <dbl> 90.9, 95.0, 95.0, 94.2, 95.0, 8…
## $ Series_Complete_Janssen <dbl> 2361, 39918, 384642, 66056, 611…
## $ Series_Complete_Moderna <dbl> 12724, 204498, 1968460, 371324,…
## $ Series_Complete_Pfizer <dbl> 3164, 318781, 3216940, 691089, …
## $ Series_Complete_Novavax <dbl> 0, 2, 38, 1, 52, 1, 6, 6, 38, 2…
## $ Series_Complete_Unk_Manuf <dbl> 82, 70, 290, 215, 602, 3, 591, …
## $ Series_Complete_Janssen_5Plus <dbl> 2361, 39914, 384637, 66028, 611…
## $ Series_Complete_Moderna_5Plus <dbl> 12724, 204289, 1955713, 370535,…
## $ Series_Complete_Pfizer_5Plus <dbl> 3163, 318775, 3210586, 690007, …
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 82, 70, 289, 215, 587, 3, 591, …
## $ Series_Complete_Janssen_12Plus <dbl> 2361, 39912, 384612, 66026, 611…
## $ Series_Complete_Moderna_12Plus <dbl> 12724, 204257, 1953836, 370413,…
## $ Series_Complete_Pfizer_12Plus <dbl> 2074, 295572, 2940222, 635307, …
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 82, 69, 286, 191, 572, 3, 588, …
## $ Series_Complete_Janssen_18Plus <dbl> 2361, 39882, 383306, 65839, 611…
## $ Series_Complete_Moderna_18Plus <dbl> 12723, 204149, 1948279, 369555,…
## $ Series_Complete_Pfizer_18Plus <dbl> 625, 259525, 2564603, 555015, 4…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 82, 64, 262, 159, 543, 3, 574, …
## $ Series_Complete_Janssen_65Plus <dbl> 227, 5079, 74665, 11821, 6832, …
## $ Series_Complete_Moderna_65Plus <dbl> 1542, 74263, 531381, 111196, 86…
## $ Series_Complete_Pfizer_65Plus <dbl> 40, 71727, 559321, 129727, 1005…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 2, 25, 80, 21, 162, 0, 263, 69,…
## $ Additional_Doses <dbl> 12048, 248903, 2987198, 646528,…
## $ Additional_Doses_Vax_Pct <dbl> 65.7, 44.2, 53.6, 57.3, 56.1, 5…
## $ Additional_Doses_5Plus <dbl> 12048, 248900, 2987162, 646515,…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 65.7, 44.2, 53.8, 57.4, 56.2, 5…
## $ Additional_Doses_12Plus <dbl> 11872, 246180, 2938665, 637193,…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 68.9, 45.6, 55.7, 59.4, 58.4, 5…
## $ Additional_Doses_18Plus <dbl> 11181, 236970, 2791202, 606621,…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 70.8, 47.1, 57.0, 61.2, 60.1, 5…
## $ Additional_Doses_50Plus <dbl> 4815, 163923, 1630617, 376685, …
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 80.1, 58.2, 66.0, 75.0, 71.4, 7…
## $ Additional_Doses_65Plus <dbl> 1575, 98849, 840257, 208155, 15…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 87.0, 65.4, 72.1, 82.4, 79.0, 7…
## $ Additional_Doses_Moderna <dbl> 10870, 109000, 1349812, 272149,…
## $ Additional_Doses_Pfizer <dbl> 1176, 136782, 1609614, 367759, …
## $ Additional_Doses_Janssen <dbl> 2, 3093, 27721, 6500, 5296, 217…
## $ Additional_Doses_Unk_Manuf <dbl> 0, 26, 45, 118, 129, 0, 438, 78…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 1126, 53725, 590595, 170399, 10…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 23.4, 32.8, 36.2, 45.2, 34.4, 1…
## $ Second_Booster_65Plus <dbl> 383, 39382, 379347, 111314, 667…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 24.3, 39.8, 45.1, 53.5, 43.5, 2…
## $ Second_Booster_Janssen <dbl> 0, 27, 253, 120, 151, 1, 119, 2…
## $ Second_Booster_Moderna <dbl> 1148, 24919, 309097, 87335, 498…
## $ Second_Booster_Pfizer <dbl> 22, 30731, 314869, 91565, 57827…
## $ Second_Booster_Unk_Manuf <dbl> 0, 2, 10, 15, 53, 0, 80, 28, 21…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.53e+10 5.16e+8 93993694 1025546 56522
## 2 after 3.51e+10 5.14e+8 92929415 1019927 48858
## 3 pctchg 6.80e- 3 4.57e-3 0.0113 0.00548 0.136
##
##
## Processed for cdcDaily:
## Rows: 48,858
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2021-09-01, 2021-03-08, 2021-09-17…
## $ state <chr> "KS", "ND", "ND", "IN", "FL", "TN", "IA", "SD", "HI", "MA",…
## $ tot_cases <dbl> 297229, 163565, 118491, 668765, 3510205, 64885, 20015, 1226…
## $ tot_deaths <dbl> 4851, 1907, 1562, 12710, 56036, 749, 561, 1967, 17, 17818, …
## $ new_cases <dbl> 0, 589, 536, 487, 9979, 1816, 317, 28, 8, 451, 1040, 133, 0…
## $ new_deaths <dbl> 0, 9, 1, 7, 294, 8, 12, 0, 0, 5, 4, 0, 0, 5, 1, 3, 0, 0, 22…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.11e+7 4.45e+7 1229807 49367
## 2 after 5.09e+7 4.43e+7 1205197 47181
## 3 pctchg 5.37e-3 5.13e-3 0.0200 0.0443
##
##
## Processed for cdcHosp:
## Rows: 47,181
## Columns: 5
## $ date <date> 2021-01-06, 2021-01-06, 2020-12-31, 2020-12-30, 2020-12-29…
## $ state <chr> "MA", "OR", "SD", "RI", "OR", "OH", "LA", "WV", "VT", "WY",…
## $ inp <dbl> 2232, 583, 282, 471, 626, 5534, 1461, 242, 0, 71, 1, 91, 49…
## $ hosp_adult <dbl> 2209, 568, 280, 469, 615, 5443, 1449, 241, 0, 70, 1, 90, 48…
## $ hosp_ped <dbl> 23, 15, 2, 2, 11, 91, 12, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.14e+11 1.70e+11 1511640. 4.31e+10 2.24e+6 1.57e+11 1.78e+6 3.62e+4
## 2 after 2.00e+11 8.22e+10 1265373. 2.09e+10 1.98e+6 7.61e+10 1.51e+6 2.86e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,611
## Columns: 9
## $ date <date> 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-3…
## $ state <chr> "SD", "MA", "HI", "RI", "MT", "WY", "LA", "KS", "IN", "MS"…
## $ vxa <dbl> 1511407, 15773792, 3156198, 2353711, 1675440, 778457, 6536…
## $ vxc <dbl> 563276, 5570460, 1128707, 899544, 618143, 300240, 2526855,…
## $ vxcpoppct <dbl> 63.7, 80.8, 79.7, 84.9, 57.8, 51.9, 54.4, 63.3, 56.8, 53.0…
## $ vxcgte65 <dbl> 151094, 1165453, 252765, 194268, 180238, 84688, 642150, 44…
## $ vxcgte65pct <dbl> 95.0, 95.0, 94.2, 95.0, 87.3, 85.4, 86.7, 94.6, 88.5, 85.2…
## $ vxcgte18 <dbl> 503622, 4896487, 990569, 794734, 562722, 275699, 2323648, …
## $ vxcgte18pct <dbl> 75.4, 88.4, 88.8, 93.0, 67.0, 62.0, 65.2, 74.2, 67.0, 63.3…
##
## Integrated per capita data file:
## Rows: 49,071
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_220907, ovrWriteError=FALSE)The function is run to download and process the latest hospitalization data:
# Run for latest data, save as RDS
indivHosp_20220907 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 269456 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 269,456
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 5077
## 2 Critical Access Hospitals 72331
## 3 Long Term 18519
## 4 Short Term 173529
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 54
## 2 GU 106
## 3 MP 46
## 4 PR 2864
## 5 VI 106
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 64869 204079 508 269456
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 143 247227 22086 269456
## 3 icu_beds_used_7_day_avg 64 237310 32082 269456
## 4 inpatient_beds_7_day_avg 67 268366 1023 269456
## 5 inpatient_beds_used_7_day_avg 51 248042 21363 269456
## 6 inpatient_beds_used_covid_7_day_avg 32 182000 87424 269456
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 162 184312 84982 269456
## 8 total_adult_patients_hospitalized_confirmed_and… 121 181944 87391 269456
## 9 total_beds_7_day_avg 63149 206009 298 269456
## 10 total_icu_beds_7_day_avg 74 255402 13980 269456
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220907, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220907.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLPost-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220907 <- postProcessCDCDaily(cdc_daily_220907,
dataThruLabel="Aug 2022",
keyDatesBurden=c("2022-08-31", "2022-02-28",
"2021-08-31", "2021-02-28"
),
keyDatesVaccine=c("2022-08-31", "2022-03-31",
"2021-10-31", "2021-05-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220907 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220907,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220907$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.70e4 49367
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.86e5 49367
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.12e5 49367
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 4.96e5 49367
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 7.91e5 49367
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.04e6 49367
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.04e6 49367
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.32e5 49367
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.83e4 49367
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.56e5 49367
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.35e5 49367
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.39e5 49367
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.37e5 49367
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.38e5 49367
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.19e5 49367
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.55e5 49367
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.67e5 49367
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.74e5 49367saveToRDS(burdenPivotList_220907, ovrWriteError=FALSE)
saveToRDS(hospPerCap_220907, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220907)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 7,740 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 7,730 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity is updated using a mix of old data (for 2021) and new data:
identical(names(indivHosp_20220907), names(readFromRDS("indivHosp_20220704")))## [1] TRUEmodHospData <- bind_rows(filter(readFromRDS("indivHosp_20220704"), lubridate::year(collection_week)<2022),
filter(indivHosp_20220907, lubridate::year(collection_week)>=2022),
.id="src"
)
updated_modStateHosp_20220907 <- hospitalCapacityCDCDaily(modHospData,
plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
)## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
Data availability by source and time is assessed:
# Temporary function to aggregate data
tempCounter <- function(df) {
df %>%
select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
colRenamer(vecRename=hhsMapper) %>%
pivot_longer(-c(hospital_pk, collection_week)) %>%
filter(!is.na(value), value>0) %>%
count(collection_week, name)
}
dfTemp <- bind_rows(tempCounter(indivHosp_20220907), tempCounter(readFromRDS("indivHosp_20220704")), .id="src")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Number of hospitals in US reporting >0 on metric by week", x=NULL, y="# Hospitals Reporting > 0") +
scale_color_discrete("Data Source:")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
group_by(collection_week, name) %>%
summarize(delta=sum(ifelse(src=="SEP-2022", n, 0)-ifelse(src!="SEP-2022", n, 0)), .groups="drop") %>%
ggplot(aes(x=collection_week, y=delta)) +
geom_line(aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))) +
geom_hline(yintercept=0, lty=2) +
geom_vline(xintercept=c(as.Date("2021-08-20"), as.Date("2022-06-24")), lty=2) +
scale_color_identity(NULL) +
facet_wrap(~name) +
labs(title="Delta in Number of hospitals in US reporting >0 on metric by week",
subtitle="Trend break dashed lines at 2021-08-20 and 2022-06-24",
x=NULL,
y="Delta in # Hospitals Reporting > 0"
)
The process is converted to functional form:
multiSourceDataCombine <- function(lst, timeVec, keyVar="collection_week", idName="src") {
# FUNCTION ARGUMENTS:
# lst: list of data frames to be combined
# timeVec: vector of time cut points (data before timeVec[1] taken from lst[[1]], etc.)
# keyVar: variable describing time in the data
# idName: name of column for .id when files combined
# Check list lengths
if(length(lst)==0) {
cat("\nEmpty list passed, returning 0x0 tibble\n")
return(tibble::tibble())
} else if (length(lst)==1) {
cat("\nList of length 1 passed, returning item in list as-is")
return(lst[[1]])
}
# Check that timeVec matches
if(length(lst) != length(timeVec) + 1) stop("\nMismatch of lst and timeVec\n")
# Check that all data frames have the same column names in the same order
vecNames <- names(lst[[1]])
for(n in 2:length(lst)) if(!isTRUE(identical(names(lst[[n]]), vecNames))) stop("\nName mismatch in files\n")
# Combine data
bind_rows(lst, .id=idName) %>%
mutate(srcNum=as.integer(get(idName)),
dateMin=ifelse(srcNum==1, NA, timeVec[srcNum-1]),
dateMax=ifelse(srcNum==max(srcNum), NA, timeVec[srcNum])
) %>%
filter(is.na(dateMin) | get(keyVar) >= dateMin,
is.na(dateMax) | get(keyVar) < dateMax
) %>%
select(-srcNum, -dateMin, -dateMax)
}
# Create modified hospital data
multiSourceHosp_20220902 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
indivHosp_20220907
),
timeVec=as.Date("2022-01-01")
)
# Confirm that function produces expected output
multiSourceHosp_20220902 %>%
select(-src) %>%
identical(modHospData %>% select(-src))## [1] TRUEThe updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20220902 <- hospitalCapacityCDCDaily(multiSourceHosp_20220902,
plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
)
Data Refreshes
Data From 2022-10-02
The latest CDC case, hospitalization, and death data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_221002.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221002.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221002.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220907")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220907")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220907")$dfRaw$vax
)
cdc_daily_221002 <- readRunCDCDaily(thruLabel="Sep 30, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 58980 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-04 new_deaths 109 20 89 1.37984496
## 2 2022-09-03 new_deaths 116 24 92 1.31428571
## 3 2022-08-27 new_deaths 163 91 72 0.56692913
## 4 2022-09-05 new_deaths 106 60 46 0.55421687
## 5 2022-08-28 new_deaths 117 87 30 0.29411765
## 6 2022-08-20 new_deaths 139 111 28 0.22400000
## 7 2021-03-03 new_deaths 1899 1547 352 0.20429483
## 8 2022-08-06 new_deaths 174 142 32 0.20253165
## 9 2022-08-13 new_deaths 137 112 25 0.20080321
## 10 2020-09-14 new_deaths 376 453 77 0.18576598
## 11 2021-03-02 new_deaths 1134 1357 223 0.17904456
## 12 2022-08-21 new_deaths 122 102 20 0.17857143
## 13 2022-07-02 new_deaths 146 124 22 0.16296296
## 14 2022-09-01 new_deaths 499 428 71 0.15318231
## 15 2022-07-30 new_deaths 151 130 21 0.14946619
## 16 2022-07-04 new_deaths 120 138 18 0.13953488
## 17 2021-03-18 new_deaths 961 843 118 0.13082040
## 18 2022-06-27 new_deaths 259 295 36 0.12996390
## 19 2022-09-02 new_deaths 509 453 56 0.11642412
## 20 2022-08-14 new_deaths 115 103 12 0.11009174
## 21 2022-05-30 new_deaths 78 86 8 0.09756098
## 22 2022-04-25 new_deaths 181 199 18 0.09473684
## 23 2021-03-19 new_deaths 1080 1182 102 0.09018568
## 24 2022-07-09 new_deaths 120 110 10 0.08695652
## 25 2022-08-25 new_deaths 536 492 44 0.08560311
## 26 2022-08-30 new_deaths 633 582 51 0.08395062
## 27 2022-04-04 new_deaths 325 353 28 0.08259587
## 28 2022-08-26 new_deaths 674 621 53 0.08185328
## 29 2022-07-16 new_deaths 163 151 12 0.07643312
## 30 2022-08-01 new_deaths 402 433 31 0.07425150
## 31 2021-10-11 new_deaths 983 915 68 0.07165437
## 32 2022-06-20 new_deaths 140 150 10 0.06896552
## 33 2021-12-28 new_deaths 2334 2185 149 0.06594379
## 34 2022-08-31 new_deaths 778 830 52 0.06467662
## 35 2020-09-15 new_deaths 827 777 50 0.06234414
## 36 2020-09-21 new_deaths 547 580 33 0.05856256
## 37 2021-02-28 new_deaths 1027 1088 61 0.05768322
## 38 2022-08-19 new_deaths 622 588 34 0.05619835
## 39 2021-03-01 new_deaths 1333 1262 71 0.05472062
## 40 2021-06-28 new_deaths 183 193 10 0.05319149
## 41 2021-10-04 new_deaths 1298 1234 64 0.05055292
## 42 2022-09-03 new_cases 26751 14942 11809 0.56647399
## 43 2022-09-04 new_cases 26642 18143 8499 0.37954672
## 44 2022-08-27 new_cases 31520 27723 3797 0.12818392
## 45 2022-08-20 new_cases 31149 27757 3392 0.11516654
## 46 2022-07-04 new_cases 46367 50779 4412 0.09083236
## 47 2022-08-06 new_cases 36583 33586 2997 0.08542234
## 48 2022-08-28 new_cases 29299 26941 2358 0.08385491
## 49 2022-08-13 new_cases 33397 30736 2661 0.08298380
## 50 2022-07-30 new_cases 41625 38338 3287 0.08221302
## 51 2022-08-29 new_cases 80569 87383 6814 0.08114223
## 52 2022-08-14 new_cases 25021 23110 1911 0.07940828
## 53 2022-08-21 new_cases 30913 28587 2326 0.07818487
## 54 2022-07-11 new_cases 115394 124421 9027 0.07528303
## 55 2022-09-01 new_cases 106616 99807 6809 0.06597133
## 56 2021-10-04 new_cases 92025 86161 5864 0.06581886
## 57 2021-09-20 new_cases 98599 92534 6065 0.06346366
## 58 2022-07-09 new_cases 58075 54584 3491 0.06197463
## 59 2022-07-23 new_cases 56226 52972 3254 0.05959816
## 60 2022-09-02 new_cases 107474 101329 6145 0.05885931
## 61 2022-07-02 new_cases 51723 48818 2905 0.05778737
## 62 2022-08-07 new_cases 37208 35135 2073 0.05731031
## 63 2021-09-06 new_cases 115878 109526 6352 0.05636102
## 64 2022-07-18 new_cases 103308 109131 5823 0.05482044
## 65 2021-09-13 new_cases 119108 112822 6286 0.05420601
## 66 2022-07-31 new_cases 42016 39803 2213 0.05409501
## 67 2021-09-27 new_cases 107419 101840 5579 0.05332148
## 68 2022-07-16 new_cases 58177 55197 2980 0.05256937
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VA tot_deaths 9582222 9605584 23362 0.002435088
## 2 KY tot_deaths 7519333 7506070 13263 0.001765410
## 3 VA tot_cases 726116885 728276276 2159391 0.002969474
## 4 KY new_deaths 16974 16757 217 0.012866503
## 5 AL new_deaths 20360 20203 157 0.007741045
## 6 VA new_deaths 21479 21334 145 0.006773644
## 7 FL new_deaths 80737 80209 528 0.006561207
## 8 KS new_deaths 9001 9054 53 0.005870950
## 9 NC new_deaths 26416 26338 78 0.002957122
## 10 OK new_deaths 13708 13746 38 0.002768267
## 11 SC new_deaths 18313 18263 50 0.002734033
## 12 SC new_cases 1688432 1674281 14151 0.008416419
## 13 KY new_cases 1559169 1546719 12450 0.008017031
## 14 NC new_cases 3143278 3123308 19970 0.006373486
## 15 CO new_cases 1637839 1634396 3443 0.002104372
## 16 WA new_cases 1789708 1787212 2496 0.001395614
##
##
##
## Raw file for cdcDaily:
## Rows: 58,980
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2020-04-07, 2020-04-08, 2020-0…
## $ state <chr> "KS", "ND", "AS", "AR", "AR", "ND", "IN", "AR", "NY", "…
## $ tot_cases <dbl> 297229, 163565, 0, 1071, 0, 118491, 668765, 56199, 1882…
## $ conf_cases <dbl> 241035, 135705, NA, NA, NA, 107475, NA, NA, NA, 144788,…
## $ prob_cases <dbl> 56194, 27860, NA, NA, NA, 11016, NA, NA, NA, 29179, 125…
## $ new_cases <dbl> 0, 589, 0, 78, 0, 536, 487, 547, 318, 667, 154, 1509, 0…
## $ pnew_case <dbl> 0, 220, NA, NA, NA, 66, 0, 0, 0, 274, 43, 0, 0, 616, 0,…
## $ tot_deaths <dbl> 4851, 1907, 0, 18, 0, 1562, 12710, 674, 8822, 2911, 115…
## $ conf_death <dbl> NA, NA, NA, NA, NA, NA, 12315, NA, NA, 2482, 9205, NA, …
## $ prob_death <dbl> NA, NA, NA, NA, NA, NA, 395, NA, NA, 429, 2325, NA, 152…
## $ new_deaths <dbl> 0, 9, 0, 0, 0, 1, 7, 11, 2, 8, 5, 6, 0, 100, 0, 5, 0, 0…
## $ pnew_death <dbl> 0, 0, NA, NA, NA, 0, 2, 0, 0, 3, 1, 0, 0, 34, 0, 0, 0, …
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "04…
## $ consent_cases <chr> "Agree", "Agree", NA, "Not agree", "Not agree", "Agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Not agree", "Not …## Rows: 50717 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-06 inp 35899 31213 4686 0.1396472
## 2 2020-07-25 hosp_ped 4543 3964 579 0.1361232
## 3 2022-09-06 hosp_ped 1651 1543 108 0.0676268
## 4 2022-09-06 hosp_adult 34457 29670 4787 0.1492975
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY inp 816280 813897 2383 0.002923609
## 2 NC inp 1500803 1499030 1773 0.001182066
## 3 ME hosp_ped 2525 2573 48 0.018830914
## 4 SC hosp_ped 9677 9528 149 0.015516793
## 5 WV hosp_ped 6031 6124 93 0.015302345
## 6 DE hosp_ped 5656 5585 71 0.012632328
## 7 KY hosp_ped 21854 21680 174 0.007993752
## 8 MS hosp_ped 12564 12470 94 0.007509787
## 9 NJ hosp_ped 20426 20300 126 0.006187693
## 10 MA hosp_ped 13350 13270 80 0.006010518
## 11 MD hosp_ped 18441 18334 107 0.005819171
## 12 UT hosp_ped 10918 10980 62 0.005662618
## 13 VA hosp_ped 19166 19260 94 0.004892521
## 14 NM hosp_ped 8390 8351 39 0.004659220
## 15 ID hosp_ped 4294 4275 19 0.004434590
## 16 CO hosp_ped 22949 23039 90 0.003914065
## 17 KS hosp_ped 5109 5128 19 0.003712025
## 18 NV hosp_ped 5680 5661 19 0.003350675
## 19 AR hosp_ped 13462 13506 44 0.003263127
## 20 TN hosp_ped 23438 23376 62 0.002648780
## 21 PA hosp_ped 57278 57151 127 0.002219717
## 22 RI hosp_ped 3719 3727 8 0.002148805
## 23 MT hosp_ped 3511 3518 7 0.001991748
## 24 IL hosp_ped 46107 46022 85 0.001845239
## 25 PR hosp_ped 24430 24470 40 0.001635992
## 26 MO hosp_ped 41624 41556 68 0.001635008
## 27 SD hosp_ped 4436 4443 7 0.001576754
## 28 NC hosp_ped 31811 31761 50 0.001573020
## 29 AL hosp_ped 21961 21929 32 0.001458191
## 30 GA hosp_ped 55159 55238 79 0.001431198
## 31 AK hosp_ped 2922 2926 4 0.001367989
## 32 KY hosp_adult 743511 741160 2351 0.003167032
## 33 NC hosp_adult 1376148 1374437 1711 0.001244099
## 34 VT hosp_adult 25440 25466 26 0.001021491
##
##
##
## Raw file for cdcHosp:
## Rows: 50,717
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Warning: One or more parsing issues, see `problems()` for details## Rows: 36440 Columns: 101
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (93): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## lgl (6): Second_Booster, Administered_Bivalent, Admin_Bivalent_PFR, Admin_B...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Administered_Bivalent Admin_Bivalent_PFR Admin_Bivalent_MOD Dist_Bivalent_PFR Dist_Bivalent_MOD
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,440
## Columns: 101
## $ date <date> 2022-09-28, 2022-09-28, 2022-0…
## $ MMWR_week <dbl> 39, 39, 39, 39, 39, 39, 39, 39,…
## $ state <chr> "VA2", "NE", "DE", "WV", "IA", …
## $ Distributed <dbl> 8845320, 4747440, 2836355, 4821…
## $ Distributed_Janssen <dbl> 626900, 151900, 101200, 170200,…
## $ Distributed_Moderna <dbl> 4313180, 1627380, 1078600, 1924…
## $ Distributed_Pfizer <dbl> 3898140, 2963760, 1653155, 2719…
## $ Distributed_Novavax <dbl> 7100, 4400, 3400, 7000, 12500, …
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 0, 245421, 291277, 269043, 2530…
## $ Distributed_Per_100k_5Plus <dbl> 0, 263231, 308620, 283773, 2698…
## $ Distributed_Per_100k_12Plus <dbl> 0, 293521, 337739, 309509, 2983…
## $ Distributed_Per_100k_18Plus <dbl> 0, 325539, 368266, 336571, 3288…
## $ Distributed_Per_100k_65Plus <dbl> 0, 1519390, 1501460, 1313760, 1…
## $ vxa <dbl> 7846098, 3451226, 1970701, 2901…
## $ Administered_5Plus <dbl> 7845919, 3437511, 1963888, 2896…
## $ Administered_12Plus <dbl> 7845897, 3306108, 1902046, 2841…
## $ Administered_18Plus <dbl> 7842493, 3086344, 1785352, 2709…
## $ Administered_65Plus <dbl> 4223864, 970237, 620955, 983314…
## $ Administered_Janssen <dbl> 256285, 95847, 62987, 68294, 18…
## $ Administered_Moderna <dbl> 4052239, 1227878, 756774, 12486…
## $ Administered_Pfizer <dbl> 3537422, 2118721, 1148333, 1582…
## $ Administered_Novavax <dbl> 111, 194, 88, 95, 271, 193, 206…
## $ Administered_Unk_Manuf <dbl> 41, 8586, 2519, 2161, 1283, 462…
## $ Admin_Per_100k <dbl> 0, 178413, 202380, 161918, 1748…
## $ Admin_Per_100k_5Plus <dbl> 0, 190599, 213688, 170493, 1856…
## $ Admin_Per_100k_12Plus <dbl> 0, 204408, 226486, 182420, 1987…
## $ Admin_Per_100k_18Plus <dbl> 0, 211635, 231806, 189157, 2064…
## $ Admin_Per_100k_65Plus <dbl> 0, 310518, 328711, 267925, 3140…
## $ Recip_Administered <dbl> 7846098, 3471776, 1946433, 2908…
## $ Administered_Dose1_Recip <dbl> 3545905, 1393418, 835939, 11911…
## $ Administered_Dose1_Pop_Pct <dbl> 0.0, 72.0, 85.8, 66.5, 69.5, 62…
## $ Administered_Dose1_Recip_5Plus <dbl> 3545797, 1385254, 832301, 11881…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 0.0, 76.8, 90.6, 69.9, 73.6, 66…
## $ Administered_Dose1_Recip_12Plus <dbl> 3545781, 1321056, 800850, 11585…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 0.0, 81.7, 95.0, 74.4, 78.2, 72…
## $ Administered_Dose1_Recip_18Plus <dbl> 3543854, 1221971, 749018, 10956…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 0.0, 83.8, 95.0, 76.5, 80.5, 75…
## $ Administered_Dose1_Recip_65Plus <dbl> 1729338, 313542, 218259, 347461…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 94.7, 95.0, 95…
## $ vxc <dbl> 2988861, 1257663, 694914, 10549…
## $ vxcpoppct <dbl> 0.0, 65.0, 71.4, 58.9, 63.2, 55…
## $ Series_Complete_5Plus <dbl> 2988790, 1254345, 693868, 10537…
## $ Series_Complete_5PlusPop_Pct <dbl> 0.0, 69.5, 75.5, 62.0, 67.3, 59…
## $ Series_Complete_12Plus <dbl> 2988784, 1198619, 668526, 10302…
## $ Series_Complete_12PlusPop_Pct <dbl> 0.0, 74.1, 79.6, 66.1, 71.6, 63…
## $ vxcgte18 <dbl> 2987373, 1108987, 624194, 97492…
## $ vxcgte18pct <dbl> 0.0, 76.0, 81.0, 68.1, 73.7, 66…
## $ vxcgte65 <dbl> 1528522, 292897, 187725, 316675…
## $ vxcgte65pct <dbl> 0.0, 93.7, 95.0, 86.3, 94.7, 88…
## $ Series_Complete_Janssen <dbl> 233980, 89549, 57869, 61920, 16…
## $ Series_Complete_Moderna <dbl> 1467192, 424332, 242886, 432285…
## $ Series_Complete_Pfizer <dbl> 1287661, 741342, 392832, 559945…
## $ Series_Complete_Novavax <dbl> 24, 59, 29, 30, 50, 91, 651, 28…
## $ Series_Complete_Unk_Manuf <dbl> 3, 2100, 861, 589, 643, 1010, 4…
## $ Series_Complete_Janssen_5Plus <dbl> 233974, 89532, 57865, 61903, 16…
## $ Series_Complete_Moderna_5Plus <dbl> 1467169, 421449, 242494, 431420…
## $ Series_Complete_Pfizer_5Plus <dbl> 1287620, 741211, 392619, 559781…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 3, 2094, 861, 587, 642, 1010, 4…
## $ Series_Complete_Janssen_12Plus <dbl> 233973, 89519, 57858, 61901, 16…
## $ Series_Complete_Moderna_12Plus <dbl> 1467168, 421331, 242451, 431343…
## $ Series_Complete_Pfizer_12Plus <dbl> 1287616, 685669, 367340, 536395…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 3, 2041, 848, 572, 606, 1004, 4…
## $ Series_Complete_Janssen_18Plus <dbl> 233938, 89441, 57808, 61838, 16…
## $ Series_Complete_Moderna_18Plus <dbl> 1467129, 421072, 242277, 430941…
## $ Series_Complete_Pfizer_18Plus <dbl> 1286279, 596543, 323272, 481581…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 3, 1877, 808, 533, 554, 996, 43…
## $ Series_Complete_Janssen_65Plus <dbl> 76712, 7058, 10082, 9689, 14679…
## $ Series_Complete_Moderna_65Plus <dbl> 835134, 141612, 77853, 162729, …
## $ Series_Complete_Pfizer_65Plus <dbl> 616669, 143226, 99394, 144029, …
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 2, 993, 393, 223, 163, 373, 227…
## $ Additional_Doses <dbl> 1221492, 673360, 339672, 506667…
## $ Additional_Doses_Vax_Pct <dbl> 40.9, 53.5, 48.9, 48.0, 55.3, 4…
## $ Additional_Doses_5Plus <dbl> 1221487, 673298, 339670, 506630…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 40.9, 53.7, 49.0, 48.1, 55.4, 4…
## $ Additional_Doses_12Plus <dbl> 1221486, 662455, 335972, 504088…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 40.9, 55.3, 50.3, 48.9, 56.9, 4…
## $ Additional_Doses_18Plus <dbl> 1221397, 631848, 322036, 491562…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 40.9, 57.0, 51.6, 50.4, 58.8, 4…
## $ Additional_Doses_50Plus <dbl> 1106281, 400915, 227926, 357348…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 46.9, 70.2, 63.2, 61.0, 71.9, 6…
## $ Additional_Doses_65Plus <dbl> 795759, 230950, 135191, 218704,…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 52.1, 78.9, 72.0, 69.1, 80.6, 7…
## $ Additional_Doses_Moderna <dbl> 646719, 255267, 142652, 234519,…
## $ Additional_Doses_Pfizer <dbl> 553511, 410083, 191595, 266805,…
## $ Additional_Doses_Janssen <dbl> 21262, 7100, 5321, 5212, 14337,…
## $ Additional_Doses_Unk_Manuf <dbl> 0, 887, 103, 129, 181, 180, 106…
## $ Second_Booster <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 289139, 153105, 88562, 115683, …
## $ Second_Booster_50Plus_Vax_Pct <dbl> 26.1, 38.2, 38.9, 32.4, 40.6, 3…
## $ Second_Booster_65Plus <dbl> 226537, 105198, 63069, 82695, 2…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 28.5, 45.6, 46.7, 37.8, 49.3, 4…
## $ Second_Booster_Janssen <dbl> 58, 114, 88, 72, 100, 94, 2151,…
## $ Second_Booster_Moderna <dbl> 152100, 62272, 43047, 57960, 14…
## $ Second_Booster_Pfizer <dbl> 141364, 105351, 52049, 67207, 1…
## $ Second_Booster_Unk_Manuf <dbl> 1, 342, 27, 35, 139, 45, 4088, …
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.77e+10 5.43e+8 95527960 1036655 57997
## 2 after 3.75e+10 5.40e+8 94432864 1030862 50133
## 3 pctchg 7.08e- 3 4.61e-3 0.0115 0.00559 0.136
##
##
## Processed for cdcDaily:
## Rows: 50,133
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2020-04-08, 2020-02-04, 2021-09-01…
## $ state <chr> "KS", "ND", "AR", "AR", "ND", "IN", "AR", "AL", "NM", "UT",…
## $ tot_cases <dbl> 297229, 163565, 1071, 0, 118491, 668765, 56199, 547966, 602…
## $ tot_deaths <dbl> 4851, 1907, 18, 0, 1562, 12710, 674, 11530, 8318, 3787, 107…
## $ new_cases <dbl> 0, 589, 78, 0, 536, 487, 547, 154, 1509, 0, 2135, 8, 451, 0…
## $ new_deaths <dbl> 0, 9, 0, 0, 1, 7, 11, 5, 6, 0, 100, 0, 5, 0, 0, 7, 8, 39, 1…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.19e+7 4.52e+7 1269286 50717
## 2 after 5.16e+7 4.50e+7 1244077 48456
## 3 pctchg 5.41e-3 5.18e-3 0.0199 0.0446
##
##
## Processed for cdcHosp:
## Rows: 48,456
## Columns: 5
## $ date <date> 2021-01-13, 2021-01-13, 2021-01-13, 2021-01-10, 2021-01-09…
## $ state <chr> "DC", "NH", "NV", "RI", "MA", "SD", "RI", "MN", "RI", "SD",…
## $ inp <dbl> 371, 294, 1817, 449, 2075, 247, 483, 1040, 471, 291, 669, 5…
## $ hosp_adult <dbl> 343, 291, 1810, 446, 2051, 244, 479, 1024, 469, 287, 665, 5…
## $ hosp_ped <dbl> 28, 3, 7, 3, 24, 3, 4, 16, 2, 4, 4, 108, 2, 1, 0, 7, 0, 1, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.19e+11 1.72e+11 1527989. 4.35e+10 2.26e+6 1.59e+11 1.80e+6 3.64e+4
## 2 after 2.02e+11 8.31e+10 1278920. 2.11e+10 2.00e+6 7.69e+10 1.52e+6 2.88e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,815
## Columns: 9
## $ date <date> 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-2…
## $ state <chr> "NE", "DE", "WV", "IA", "ID", "FL", "ND", "AR", "KY", "MI"…
## $ vxa <dbl> 3451226, 1970701, 2901813, 5515503, 2630561, 39803100, 117…
## $ vxc <dbl> 1257663, 694914, 1054914, 1994024, 989510, 14697269, 43395…
## $ vxcpoppct <dbl> 65.0, 71.4, 58.9, 63.2, 55.4, 68.4, 56.9, 55.8, 58.6, 61.4…
## $ vxcgte65 <dbl> 292897, 187725, 316675, 523635, 257641, 4173533, 105759, 4…
## $ vxcgte65pct <dbl> 93.7, 95.0, 86.3, 94.7, 88.6, 92.8, 88.2, 82.3, 87.9, 89.9…
## $ vxcgte18 <dbl> 1108987, 624194, 974923, 1790031, 891754, 13500109, 393211…
## $ vxcgte18pct <dbl> 76.0, 81.0, 68.1, 73.7, 66.6, 78.3, 67.6, 65.5, 68.7, 70.5…
##
## Integrated per capita data file:
## Rows: 50,346
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_221002, ovrWriteError=FALSE)The function is run to download and process the latest hospitalization data:
# Run for latest data, save as RDS
indivHosp_20221003 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 260543 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 260,543
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 4909
## 2 Critical Access Hospitals 69937
## 3 Long Term 17869
## 4 Short Term 167828
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 52
## 2 GU 102
## 3 MP 40
## 4 PR 2766
## 5 VI 104
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 71194 188847 502 260543
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 131 238657 21755 260543
## 3 icu_beds_used_7_day_avg 56 228646 31841 260543
## 4 inpatient_beds_7_day_avg 46 259479 1018 260543
## 5 inpatient_beds_used_7_day_avg 35 239484 21024 260543
## 6 inpatient_beds_used_covid_7_day_avg 26 173367 87150 260543
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 169 174858 85516 260543
## 8 total_adult_patients_hospitalized_confirmed_and… 127 173350 87066 260543
## 9 total_beds_7_day_avg 69419 190826 298 260543
## 10 total_icu_beds_7_day_avg 58 246878 13607 260543
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20221003, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_221002 <- postProcessCDCDaily(cdc_daily_221002,
dataThruLabel="Sep 2022",
keyDatesBurden=c("2022-09-30", "2022-03-31",
"2021-09-30", "2021-03-31"
),
keyDatesVaccine=c("2022-09-28", "2022-03-31",
"2021-09-30", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_221002 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_221002,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_221002$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.77e4 50717
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.90e5 50717
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.19e5 50717
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.02e5 50717
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.01e5 50717
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.05e6 50717
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.06e6 50717
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.58e5 50717
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.91e4 50717
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.61e5 50717
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.43e5 50717
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.47e5 50717
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.48e5 50717
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.55e5 50717
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.36e5 50717
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.70e5 50717
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.73e5 50717
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.88e5 50717saveToRDS(burdenPivotList_221002, ovrWriteError=FALSE)
saveToRDS(hospPerCap_221002, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_221002)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 8,040 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 8,030 more rows
## # ℹ Use `print(n = ...)` to see more rowsA function is written for the hospital data availability checks:
checkHospitalDataComplete <- function(df1=NULL,
df2=NULL,
dfAll=NULL,
lab1="DF1",
lab2="DF2",
trendBreaks=c(),
makeP1=TRUE,
makeP2=TRUE,
returnData=FALSE
) {
# FUNCTION ARGUMENTS:
# df1: the first data frame (NULL means integrated frame passed as dfAll)
# df2: the second data frame (NULL means integrated frame passed as dfAll)
# dfAll: integrated data frame from previous iteration of function (will ignore df1 and df2)
# lab1: plot label for the first data frame
# lab2: plot label for the second data frame
# trendBreaks: character vector of trend break dates of form YYYY-MM-DD - if c(), no trend brek vlines plotted
# makeP1: boolean, should the first plot be created and printed?
# makeP2: boolean, should the first plot be created and printed?
# returnData: boolean, should dfAll be returned?
# Temporary function to aggregate data
tempCountComplete <- function(df) {
df %>%
select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
colRenamer(vecRename=hhsMapper) %>%
pivot_longer(-c(hospital_pk, collection_week)) %>%
filter(!is.na(value), value>0) %>%
count(collection_week, name)
}
# Create or use passed data
if(is.null(dfAll)) {
if(is.null(df1) | is.null(df2)) stop("dfAll not passed requires both df1 and df2 to be passed\n")
dfAll <- bind_rows(tempCountComplete(df1), tempCountComplete(df2), .id="src")
} else {
if(!is.null(df1) | !is.null(df2)) warning("dfAll passed and will be used; df1 and/or df2 ignored\n")
}
# Plot data completeness - hospitals reporting >0 on metric by week
if(isTRUE(makeP1)) {
p1 <- dfAll %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfAll, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"=lab1, "2"=lab2)[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Number of hospitals in US reporting >0 on metric by week",
x=NULL,
y="# Hospitals Reporting > 0"
) +
scale_color_discrete("Data Source:")
if(length(trendBreaks) > 0) {
p1 <- p1 + geom_vline(xintercept=as.Date(all_of(trendBreaks)), lty=2) +
labs(subtitle=paste0("Trend break dashed lines at ",
paste0(all_of(trendBreaks), collapse=" and ")
)
)
}
print(p1)
}
# Plot data completeness - showing difference in reported data and trend break dates
if(isTRUE(makeP2)) {
p2 <- dfAll %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfAll, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"=lab1, "2"=lab2)[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
group_by(collection_week, name) %>%
summarize(delta=sum(ifelse(src==lab1, n, 0)-ifelse(src!=lab1, n, 0)), .groups="drop") %>%
ggplot(aes(x=collection_week, y=delta)) +
geom_line() +
geom_point(data=~filter(., delta != 0),
aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))
) +
geom_hline(yintercept=0, lty=2) +
scale_color_identity(NULL) +
facet_wrap(~name) +
labs(title="Delta in Number of hospitals in US reporting >0 on metric by week",
x=NULL,
y="Delta in # Hospitals Reporting > 0"
)
if(length(trendBreaks) > 0) {
p2 <- p2 + geom_vline(xintercept=as.Date(all_of(trendBreaks)), lty=2) +
labs(subtitle=paste0("Trend break dashed lines at ",
paste0(all_of(trendBreaks), collapse=" and ")
)
)
}
print(p2)
}
if(isTRUE(returnData)) return(dfAll)
}
# Create the data
dfTemp <- checkHospitalDataComplete(df1=readFromRDS("indivHosp_20221003"),
df2=readFromRDS("indivHosp_20220704"),
makeP1=FALSE,
makeP2=FALSE,
returnData=TRUE
)
# Create the first plot
checkHospitalDataComplete(dfAll=dfTemp,
lab1="OCT-2022",
lab2="JUL-2022",
makeP2=FALSE,
trendBreaks=c("2021-09-25", "2022-06-24")
)
# Create the second plot
checkHospitalDataComplete(dfAll=dfTemp,
lab1="OCT-2022",
lab2="JUL-2022",
makeP1=FALSE,
trendBreaks=c("2021-09-25", "2022-06-24")
)
The discontinuity issues due to occasional minor changes between positive and negative is resolved by using points colored by positive/negative and a solid line
Hospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20221002 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20221003")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20221002 <- hospitalCapacityCDCDaily(multiSourceHosp_20221002,
plotSub="Aug 2020 to Sep 2022\nOld data used pre-2022"
)
Data From 2022-11-02
The latest CDC case, hospitalization, and death data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_221102.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221102.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221102.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_221002")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_221002")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_221002")$dfRaw$vax
)
cdc_daily_221102 <- readRunCDCDaily(thruLabel="Oct 31, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 60060 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 18
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-25 new_deaths 92 38 54 0.83076923
## 2 2022-09-24 new_deaths 111 81 30 0.31250000
## 3 2022-09-18 new_deaths 83 68 15 0.19867550
## 4 2022-09-26 new_deaths 233 198 35 0.16241299
## 5 2022-09-17 new_deaths 103 89 14 0.14583333
## 6 2022-09-30 new_deaths 370 321 49 0.14182344
## 7 2022-09-10 new_deaths 140 122 18 0.13740458
## 8 2022-08-28 new_deaths 131 117 14 0.11290323
## 9 2022-09-27 new_deaths 470 429 41 0.09121246
## 10 2022-09-23 new_deaths 355 327 28 0.08211144
## 11 2022-08-21 new_deaths 132 122 10 0.07874016
## 12 2022-09-19 new_deaths 228 211 17 0.07744875
## 13 2022-09-05 new_deaths 114 106 8 0.07272727
## 14 2022-08-20 new_deaths 149 139 10 0.06944444
## 15 2022-09-29 new_deaths 605 570 35 0.05957447
## 16 2022-08-27 new_deaths 172 163 9 0.05373134
## 17 2022-09-03 new_deaths 122 116 6 0.05042017
## 18 2022-09-30 new_cases 49011 46391 2620 0.05492547
## 19 2022-09-28 new_cases 67106 63713 3393 0.05187320
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC new_deaths 26857 26531 326 0.012212482
## 2 ND new_deaths 2375 2364 11 0.004642330
## 3 FL new_deaths 81756 81441 315 0.003860365
## 4 AL new_deaths 20498 20423 75 0.003665600
## 5 NYC new_deaths 41934 41977 43 0.001024895
## 6 NC new_cases 3207665 3200475 7190 0.002244021
## 7 SC new_cases 1711020 1708074 2946 0.001723264
##
##
##
## Raw file for cdcDaily:
## Rows: 60,060
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-11-22, 2022-0…
## $ state <chr> "KS", "ND", "AS", "AL", "AK", "RMI", "ND", "PR", "PW", …
## $ tot_cases <dbl> 297229, 163565, 11, 841461, 251425, 0, 173, 173967, 0, …
## $ conf_cases <dbl> 241035, 135705, NA, 620483, NA, 0, NA, 144788, NA, NA, …
## $ prob_cases <dbl> 56194, 27860, NA, 220978, NA, 0, NA, 29179, NA, NA, 0, …
## $ new_cases <dbl> 0, 589, 0, 703, 0, 0, 14, 667, 0, 1509, 0, 28, 2293, 59…
## $ pnew_case <dbl> 0, 220, 0, 357, 0, 0, NA, 274, 0, 0, 0, 5, 552, 1266, 7…
## $ tot_deaths <dbl> 4851, 1907, 0, 16377, 1252, 0, 3, 2911, 0, 8318, 3787, …
## $ conf_death <dbl> NA, NA, NA, 12727, NA, 0, NA, 2482, NA, NA, 3635, 1601,…
## $ prob_death <dbl> NA, NA, NA, 3650, NA, 0, NA, 429, NA, NA, 152, 366, 0, …
## $ new_deaths <dbl> 0, 9, 0, 7, 0, 0, 0, 8, 0, 6, 0, 0, 0, 8, 0, 4, 0, 0, 1…
## $ pnew_death <dbl> 0, 0, 0, 3, 0, 0, NA, 3, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0,…
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases <chr> "Agree", "Agree", NA, "Agree", "N/A", "Agree", "Agree",…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Agree", "N/A", "Agree", "Not a…## Rows: 52391 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 6791 4561 2230 0.3928823
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NV inp 669396 670631 1235 0.001843246
## 2 NH hosp_ped 1328 1282 46 0.035249042
## 3 WV hosp_ped 6407 6206 201 0.031871878
## 4 ME hosp_ped 2722 2675 47 0.017417084
## 5 MA hosp_ped 14134 13890 244 0.017413645
## 6 NV hosp_ped 5928 5831 97 0.016498002
## 7 VI hosp_ped 128 130 2 0.015503876
## 8 SC hosp_ped 10059 9955 104 0.010392725
## 9 AR hosp_ped 14079 13965 114 0.008130081
## 10 AZ hosp_ped 29543 29312 231 0.007849800
## 11 TN hosp_ped 24231 24060 171 0.007082065
## 12 AL hosp_ped 22626 22485 141 0.006251247
## 13 VT hosp_ped 658 654 4 0.006097561
## 14 UT hosp_ped 11316 11255 61 0.005405166
## 15 NM hosp_ped 8584 8545 39 0.004553681
## 16 IN hosp_ped 18602 18682 80 0.004291385
## 17 KS hosp_ped 5308 5288 20 0.003775009
## 18 PR hosp_ped 25141 25055 86 0.003426568
## 19 CO hosp_ped 23478 23402 76 0.003242321
## 20 FL hosp_ped 97891 97593 298 0.003048843
## 21 MS hosp_ped 12979 13017 38 0.002923527
## 22 KY hosp_ped 22910 22845 65 0.002841220
## 23 NJ hosp_ped 20956 20999 43 0.002049815
## 24 IA hosp_ped 8413 8430 17 0.002018643
## 25 PA hosp_ped 59345 59227 118 0.001990352
## 26 GA hosp_ped 57694 57605 89 0.001543812
## 27 OK hosp_ped 28563 28528 35 0.001226113
## 28 MO hosp_ped 43011 43059 48 0.001115371
## 29 IL hosp_ped 47538 47488 50 0.001052344
## 30 OR hosp_ped 12762 12749 13 0.001019168
## 31 NV hosp_adult 616917 618249 1332 0.002156795
## 32 WV hosp_adult 345523 345909 386 0.001116523
##
##
##
## Raw file for cdcHosp:
## Rows: 52,391
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Warning: One or more parsing issues, see `problems()` for details## Rows: 36696 Columns: 101
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## lgl (5): Administered_Bivalent, Admin_Bivalent_PFR, Admin_Bivalent_MOD, Dis...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,696
## Columns: 101
## $ date <date> 2022-10-26, 2022-10-26, 2022-1…
## $ MMWR_week <dbl> 43, 43, 43, 43, 43, 43, 43, 43,…
## $ state <chr> "CO", "BP2", "DD2", "AR", "LA",…
## $ Distributed <dbl> 15839015, 389930, 7382310, 7633…
## $ Distributed_Janssen <dbl> 500100, 16200, 210700, 262300, …
## $ Distributed_Moderna <dbl> 5607840, 171620, 2394540, 31571…
## $ Distributed_Pfizer <dbl> 9713375, 202110, 4748270, 41899…
## $ Distributed_Novavax <dbl> 17700, 0, 28800, 23800, 6600, 4…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 275043, 0, 0, 252938, 206691, 2…
## $ Distributed_Per_100k_5Plus <dbl> 291881, 0, 0, 269787, 221024, 2…
## $ Distributed_Per_100k_12Plus <dbl> 320884, 0, 0, 298492, 244911, 2…
## $ Distributed_Per_100k_18Plus <dbl> 352039, 0, 0, 329350, 269818, 2…
## $ Distributed_Per_100k_65Plus <dbl> 1880200, 0, 0, 1457040, 1296680…
## $ vxa <dbl> 12152626, 335497, 9079453, 4641…
## $ Administered_5Plus <dbl> 12068391, 335495, 9063024, 4627…
## $ Administered_12Plus <dbl> 11620758, 335495, 8883529, 4499…
## $ Administered_18Plus <dbl> 10883886, 335495, 8387809, 4225…
## $ Administered_65Plus <dbl> 2933378, 11277, 857771, 1405163…
## $ Administered_Janssen <dbl> 345410, 14176, 327958, 125793, …
## $ Administered_Moderna <dbl> 4622790, 154826, 2958844, 19766…
## $ Administered_Pfizer <dbl> 7165826, 166463, 5744807, 25343…
## $ Administered_Novavax <dbl> 1712, 0, 2484, 268, 480, 295, 2…
## $ Administered_Unk_Manuf <dbl> 16888, 32, 45360, 4366, 3639, 5…
## $ Admin_Per_100k <dbl> 211029, 0, 0, 153800, 144276, 1…
## $ Admin_Per_100k_5Plus <dbl> 222396, 0, 0, 163556, 154002, 1…
## $ Admin_Per_100k_12Plus <dbl> 235426, 0, 0, 175954, 167022, 1…
## $ Admin_Per_100k_18Plus <dbl> 241906, 0, 0, 182340, 174300, 2…
## $ Admin_Per_100k_65Plus <dbl> 348212, 0, 0, 268221, 266760, 2…
## $ Recip_Administered <dbl> 12178883, 335497, 9079453, 4695…
## $ Administered_Dose1_Recip <dbl> 4745768, 152116, 4600027, 20805…
## $ Administered_Dose1_Pop_Pct <dbl> 82.4, 0.0, 0.0, 68.9, 62.4, 77.…
## $ Administered_Dose1_Recip_5Plus <dbl> 4700168, 152114, 4589610, 20718…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 86.6, 0.0, 0.0, 73.2, 66.5, 82.…
## $ Administered_Dose1_Recip_12Plus <dbl> 4480951, 152114, 4486081, 20017…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 90.8, 0.0, 0.0, 78.3, 71.7, 88.…
## $ Administered_Dose1_Recip_18Plus <dbl> 4155669, 152114, 4230192, 18630…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 92.4, 0.0, 0.0, 80.4, 74.0, 90.…
## $ Administered_Dose1_Recip_65Plus <dbl> 890491, 4115, 476135, 515480, 6…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 0.0, 0.0, 95.0, 92.3, 95.…
## $ vxc <dbl> 4175718, 138953, 3655064, 16955…
## $ vxcpoppct <dbl> 72.5, 0.0, 0.0, 56.2, 54.7, 62.…
## $ Series_Complete_5Plus <dbl> 4156753, 138952, 3652831, 16926…
## $ Series_Complete_5PlusPop_Pct <dbl> 76.6, 0.0, 0.0, 59.8, 58.4, 66.…
## $ Series_Complete_12Plus <dbl> 3972620, 138952, 3583964, 16411…
## $ Series_Complete_12PlusPop_Pct <dbl> 80.5, 0.0, 0.0, 64.2, 63.2, 71.…
## $ vxcgte18 <dbl> 3681706, 138952, 3383916, 15285…
## $ vxcgte18pct <dbl> 81.8, 0.0, 0.0, 66.0, 65.6, 73.…
## $ vxcgte65 <dbl> 808256, 3763, 251475, 435109, 6…
## $ vxcgte65pct <dbl> 95.0, 0.0, 0.0, 83.1, 86.9, 87.…
## $ Series_Complete_Janssen <dbl> 316583, 13495, 319863, 115591, …
## $ Series_Complete_Moderna <dbl> 1482213, 56214, 1106217, 684277…
## $ Series_Complete_Pfizer <dbl> 2364703, 69244, 2216621, 893449…
## $ Series_Complete_Novavax <dbl> 593, 0, 931, 150, 147, 104, 130…
## $ Series_Complete_Unk_Manuf <dbl> 4720, 0, 10757, 1304, 1348, 186…
## $ Series_Complete_Janssen_5Plus <dbl> 316563, 13494, 319842, 115470, …
## $ Series_Complete_Moderna_5Plus <dbl> 1470443, 56214, 1104733, 683178…
## $ Series_Complete_Pfizer_5Plus <dbl> 2364457, 69244, 2216589, 892657…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 4699, 0, 10736, 1295, 1343, 186…
## $ Series_Complete_Janssen_12Plus <dbl> 316553, 13494, 319830, 115462, …
## $ Series_Complete_Moderna_12Plus <dbl> 1469786, 56214, 1104614, 682949…
## $ Series_Complete_Pfizer_12Plus <dbl> 2181121, 69244, 2148140, 841426…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 4569, 0, 10450, 1248, 1306, 183…
## $ Series_Complete_Janssen_18Plus <dbl> 316207, 13494, 319494, 115013, …
## $ Series_Complete_Moderna_18Plus <dbl> 1469043, 56214, 1103520, 681652…
## $ Series_Complete_Pfizer_18Plus <dbl> 1892075, 69244, 1950664, 730710…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 3801, 0, 9316, 1173, 1259, 176,…
## $ Series_Complete_Janssen_65Plus <dbl> 29249, 196, 10615, 15940, 22522…
## $ Series_Complete_Moderna_65Plus <dbl> 383852, 1826, 84801, 243252, 30…
## $ Series_Complete_Pfizer_65Plus <dbl> 393887, 1741, 155484, 175349, 3…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 1189, 0, 563, 564, 219, 63, 878…
## $ Additional_Doses <dbl> 2342627, 52153, 966851, 730243,…
## $ Additional_Doses_Vax_Pct <dbl> 56.1, 37.5, 26.5, 43.1, 42.3, 4…
## $ Additional_Doses_5Plus <dbl> 2342527, 52153, 966774, 730158,…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 56.4, 37.5, 26.5, 43.1, 42.3, 4…
## $ Additional_Doses_12Plus <dbl> 2300475, 52153, 959937, 723815,…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 57.9, 37.5, 26.8, 44.1, 43.1, 4…
## $ Additional_Doses_18Plus <dbl> 2188216, 52153, 921716, 700345,…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 59.4, 37.5, 27.2, 45.8, 44.8, 4…
## $ Additional_Doses_50Plus <dbl> 1227186, 16410, 283327, 492070,…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 72.5, 54.6, 38.0, 58.5, 58.2, 5…
## $ Additional_Doses_65Plus <dbl> 645598, 2658, 100062, 298265, 4…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 79.9, 70.6, 39.8, 68.5, 68.3, 6…
## $ Additional_Doses_Moderna <dbl> 989679, 31561, 327343, 345696, …
## $ Additional_Doses_Pfizer <dbl> 1323953, 19927, 628820, 373658,…
## $ Additional_Doses_Janssen <dbl> 27100, 643, 6220, 10264, 13964,…
## $ Additional_Doses_Unk_Manuf <dbl> 1855, 22, 4183, 611, 260, 55, 3…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 652497, 3571, 74708, 190165, 24…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 53.2, 21.8, 26.4, 38.6, 32.1, 3…
## $ Second_Booster_65Plus <dbl> 400749, 866, 33346, 135359, 166…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 62.1, 32.6, 33.3, 45.4, 37.9, 4…
## $ Second_Booster_Janssen <dbl> 421, 3, 94, 116, 168, 53, 313, …
## $ Second_Booster_Moderna <dbl> 325593, 2532, 30870, 104368, 11…
## $ Second_Booster_Pfizer <dbl> 493405, 2152, 74296, 108959, 15…
## $ Second_Booster_Unk_Manuf <dbl> 742, 2, 595, 149, 38, 17, 67, 0…
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.95e+10 5.62e+8 96180659 1042962 59059
## 2 after 3.92e+10 5.59e+8 95065282 1037080 51051
## 3 pctchg 7.28e- 3 4.64e-3 0.0116 0.00564 0.136
##
##
## Processed for cdcDaily:
## Rows: 51,051
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2021-11-22, 2022-05-30, 2020-04-03…
## $ state <chr> "KS", "ND", "AL", "AK", "ND", "NM", "UT", "SD", "OH", "OK",…
## $ tot_cases <dbl> 297229, 163565, 841461, 251425, 173, 602931, 636992, 122688…
## $ tot_deaths <dbl> 4851, 1907, 16377, 1252, 3, 8318, 3787, 1967, 18646, 471, 1…
## $ new_cases <dbl> 0, 589, 703, 0, 14, 1509, 0, 28, 2293, 1040, 133, 133, 1028…
## $ new_deaths <dbl> 0, 9, 7, 0, 0, 6, 0, 0, 0, 4, 0, 14, 8, 0, 73, 3, 0, 0, 7, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.27e+7 4.60e+7 1311468 52391
## 2 after 5.25e+7 4.58e+7 1285571 50037
## 3 pctchg 5.43e-3 5.19e-3 0.0197 0.0449
##
##
## Processed for cdcHosp:
## Rows: 50,037
## Columns: 5
## $ date <date> 2021-01-26, 2021-01-23, 2021-01-21, 2021-01-13, 2021-01-10…
## $ state <chr> "OK", "MN", "RI", "OR", "RI", "KS", "SC", "RI", "RI", "LA",…
## $ inp <dbl> 1598, 618, 407, 512, 449, 989, 2246, 461, 483, 1619, 471, 7…
## $ hosp_adult <dbl> 1516, 598, 399, 507, 446, 986, 2232, 456, 479, 1609, 469, 7…
## $ hosp_ped <dbl> 82, 20, 8, 5, 3, 3, 14, 5, 4, 10, 2, 81, 16, 1, 9, 22, 1, 0…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.24e+11 1.74e+11 1544457. 4.40e+10 2.28e+6 1.61e+11 1.82e+6 3.67e+4
## 2 after 2.04e+11 8.40e+10 1292563. 2.13e+10 2.02e+6 7.77e+10 1.54e+6 2.90e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.15e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 29,019
## Columns: 9
## $ date <date> 2022-10-26, 2022-10-26, 2022-10-26, 2022-10-26, 2022-10-2…
## $ state <chr> "CO", "AR", "LA", "NV", "KY", "MN", "AL", "OH", "MT", "WV"…
## $ vxa <dbl> 12152626, 4641392, 6707088, 5315414, 7144109, 11838868, 66…
## $ vxc <dbl> 4175718, 1695580, 2541814, 1930260, 2634385, 4013235, 2576…
## $ vxcpoppct <dbl> 72.5, 56.2, 54.7, 62.7, 59.0, 71.2, 52.5, 59.8, 58.4, 59.2…
## $ vxcgte65 <dbl> 808256, 435109, 644253, 434579, 663968, 908701, 714593, 18…
## $ vxcgte65pct <dbl> 95.0, 83.1, 86.9, 87.6, 88.5, 95.0, 84.1, 88.9, 88.3, 86.8…
## $ vxcgte18 <dbl> 3681706, 1528584, 2334821, 1747560, 2393411, 3494198, 2386…
## $ vxcgte18pct <dbl> 81.8, 66.0, 65.6, 73.2, 69.1, 80.6, 62.6, 69.2, 67.6, 68.4…
##
## Integrated per capita data file:
## Rows: 51,927
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_221102, ovrWriteError=FALSE)The CDC modified deaths and cases reporting to be weekly, based on summation of county-level data to states. Per their website, methodology has changed (inconsistency between datasets?) and the previous daily data is no longer updated as of October 20, 2022. Weekly deaths and cases data are available at CDC website
The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20221103 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20221103.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20221103.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 132411 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 132,411
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 2472
## 2 Critical Access Hospitals 35614
## 3 Long Term 8968
## 4 Short Term 85357
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 12
## 2 GU 68
## 3 MP 25
## 4 PR 1311
## 5 VI 54
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 36802 95379 230 132411
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 20208 102724 9479 132411
## 3 icu_beds_used_7_day_avg 22029 96812 13570 132411
## 4 inpatient_beds_7_day_avg 5519 126362 530 132411
## 5 inpatient_beds_used_7_day_avg 5519 116077 10815 132411
## 6 inpatient_beds_used_covid_7_day_avg 1253 88897 42261 132411
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 20215 77013 35183 132411
## 8 total_adult_patients_hospitalized_confirmed_and… 18629 77473 36309 132411
## 9 total_beds_7_day_avg 20916 111369 126 132411
## 10 total_icu_beds_7_day_avg 2231 123464 6716 132411
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20221103, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_221102 <- postProcessCDCDaily(cdc_daily_221102,
dataThruLabel="Oct 2022",
keyDatesBurden=c("2022-10-15", "2022-04-15",
"2021-10-15", "2021-04-15"
),
keyDatesVaccine=c("2022-10-26", "2022-04-30",
"2021-10-31", "2021-04-30"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_221102 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_221102,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_221102$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.83e4 52391
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.95e5 52391
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.25e5 52391
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.07e5 52391
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.11e5 52391
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.07e6 52391
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.09e6 52391
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.88e5 52391
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.00e4 52391
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.67e5 52391
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.51e5 52391
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.55e5 52391
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.61e5 52391
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.75e5 52391
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.57e5 52391
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.90e5 52391
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.77e5 52391
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.04e5 52391saveToRDS(burdenPivotList_221102, ovrWriteError=FALSE)
saveToRDS(hospPerCap_221102, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_221102)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 8,412 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 8,402 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20221102 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20221103")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20221102 <- hospitalCapacityCDCDaily(multiSourceHosp_20221102,
plotSub="Aug 2020 to Oct 2022\nOld data used pre-2022"
)
An example of the new data file is downloaded manually and then read:
tmpBurden <- fileRead("./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv")## Rows: 8820 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (4): tot_cases, new_cases, tot_deaths, new_deaths
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(tmpBurden)## Rows: 8,820
## Columns: 8
## $ date_updated <date> 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-13, 2020-02-…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK…
## $ start_date <date> 2020-01-16, 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-…
## $ end_date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2020-02-…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337, 355, …
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18, 19, 1…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 10, 10, …
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0, 0, 0, …# Check for states
tmpStateBurden <- tmpBurden %>%
arrange(end_date, state) %>%
group_by(state) %>%
summarize(across(c(new_cases, new_deaths), .fns=sum, na.rm=TRUE),
across(c(tot_cases, tot_deaths), .fns=max, na.rm=TRUE)
) %>%
ungroup()
# Are all 50 states and DC included?
tmpStateBurden %>%
filter(state %in% c(state.abb, "DC")) %>%
arrange(desc(tot_deaths)) %>%
print(n=+Inf)## # A tibble: 51 × 5
## state new_cases new_deaths tot_cases tot_deaths
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CA 11401645 96334 11401645 96334
## 2 TX 8018724 91253 8018724 91253
## 3 FL 7215695 82535 7215695 82535
## 4 PA 3329409 47934 3329409 47934
## 5 GA 2929463 40803 2929463 40803
## 6 OH 3197300 40249 3197300 40249
## 7 IL 3832739 40083 3832739 40083
## 8 MI 2907819 39574 2907819 39574
## 9 NJ 2813645 34958 2813645 34958
## 10 AZ 2300375 31613 2300375 31613
## 11 NY 3329357 30451 3329357 30451
## 12 TN 2371639 28187 2371639 28187
## 13 NC 3255540 27267 3255540 27267
## 14 IN 1948019 25013 1948019 25013
## 15 VA 2129684 22234 2129684 22234
## 16 MA 2099238 22209 2099238 22209
## 17 MO 1684433 21938 1684433 21938
## 18 AL 1540329 20608 1540329 20608
## 19 SC 1729333 18660 1729333 18660
## 20 LA 1465700 18218 1465700 18218
## 21 KY 1619549 17363 1619549 17363
## 22 MD 1275208 15603 1275208 15603
## 23 WI 1907232 15511 1907232 15511
## 24 OK 1211210 14992 1211210 14992
## 25 WA 1843926 14653 1843926 14653
## 26 MN 1694438 13558 1694438 13558
## 27 CO 1683848 13473 1683848 13473
## 28 MS 935770 13005 935770 13005
## 29 AR 962764 12523 962764 12523
## 30 NV 857473 11580 857473 11580
## 31 CT 915786 11527 915786 11527
## 32 IA 866615 10229 866615 10229
## 33 KS 893300 9156 893300 9626
## 34 OR 913809 8743 913809 8743
## 35 NM 633631 8675 633631 8675
## 36 WV 610432 7538 610432 7538
## 37 ID 502130 5237 502130 5237
## 38 UT 1050455 5066 1050455 5066
## 39 NE 537129 4606 537129 4606
## 40 RI 413725 3705 413725 3705
## 41 MT 316251 3581 316251 3581
## 42 DE 314553 3148 314553 3148
## 43 SD 265890 3078 265890 3078
## 44 NH 358819 2782 358819 2782
## 45 ME 299321 2711 299321 2711
## 46 ND 273779 2232 273779 2232
## 47 WY 179366 1917 179366 1917
## 48 HI 351617 1685 351617 1685
## 49 DC 170482 1392 170482 1402
## 50 AK 285355 1376 285355 1376
## 51 VT 145838 763 145838 763# What other states are included?
tmpStateBurden %>%
filter(!(state %in% c(state.abb, "DC"))) %>%
arrange(desc(tot_deaths)) %>%
print(n=+Inf)## # A tibble: 9 × 5
## state new_cases new_deaths tot_cases tot_deaths
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 NYC 2962479 42409 2962479 42966
## 2 PR 990366 5285 990366 5285
## 3 GU 58990 406 58990 406
## 4 VI 23435 124 23435 124
## 5 FSM 22203 58 22203 58
## 6 MP 13219 41 13219 41
## 7 AS 8257 34 8257 34
## 8 RMI 15386 17 15386 17
## 9 PW 5530 7 5530 7# Are there disconnects between total and sum of new?
tmpStateBurden %>%
filter((new_cases != tot_cases) | (new_deaths != tot_deaths)) %>%
mutate(ratCase=tot_cases/new_cases, ratDeath=tot_deaths/new_deaths)## # A tibble: 3 × 7
## state new_cases new_deaths tot_cases tot_deaths ratCase ratDeath
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DC 170482 1392 170482 1402 1 1.01
## 2 KS 893300 9156 893300 9626 1 1.05
## 3 NYC 2962479 42409 2962479 42966 1 1.01NYC data appear to be tracked separately from NY data, requiring combination. Otherwise, the expected geographical units appear to be included, and with totals and sum of new matching (exceptions for deaths in DC, NYC, and Kansas).
Similarity of total burden is compared:
tmpBurdenDate <- tmpBurden %>%
select(date=end_date, state, where(is.numeric)) %>%
bind_rows(cdc_daily_221102$dfProcess$cdcDaily, .id="src") %>%
mutate(src=c("1"="CDC weekly (new)", "2"="CDC daily (old)")[src])
tmpBurdenDate## # A tibble: 59,871 × 7
## src date state tot_cases new_cases tot_deaths new_deaths
## <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CDC weekly (new) 2020-01-22 AK 0 0 0 0
## 2 CDC weekly (new) 2020-01-29 AK 0 0 0 0
## 3 CDC weekly (new) 2020-02-05 AK 0 0 0 0
## 4 CDC weekly (new) 2020-02-12 AK 0 0 0 0
## 5 CDC weekly (new) 2020-02-19 AK 0 0 0 0
## 6 CDC weekly (new) 2020-02-26 AK 0 0 0 0
## 7 CDC weekly (new) 2020-03-04 AK 0 0 0 0
## 8 CDC weekly (new) 2020-03-11 AK 0 0 0 0
## 9 CDC weekly (new) 2020-03-18 AK 11 11 0 0
## 10 CDC weekly (new) 2020-03-25 AK 63 52 1 1
## # … with 59,861 more rows# Plot for total deaths and total cases
tmpBurdenDate %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(src, date) %>%
summarize(across(where(is.numeric), sum, na.rm=TRUE), .groups="drop") %>%
pivot_longer(where(is.numeric)) %>%
filter(name %in% c("tot_cases", "tot_deaths")) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(color=src, group=src)) +
facet_wrap(~name, scales="free_y")
# Plot by state - latest burden
tmpBurdenDate %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(src, state) %>%
summarize(across(where(is.numeric), last, order_by=date), .groups="drop") %>%
pivot_longer(where(is.numeric)) %>%
filter(name %in% c("tot_cases", "tot_deaths")) %>%
ggplot(aes(x=fct_reorder(state, value), y=value)) +
geom_point(aes(color=src)) +
facet_wrap(~name, scales="free_x") +
coord_flip() +
labs(x=NULL, y=NULL, title="Most recent burden by state and data source") +
scale_color_discrete("Source")
# Plot by state - difference in burden
tmpBurdenDate %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(src, state) %>%
summarize(across(where(is.numeric), last, order_by=date), .groups="drop") %>%
pivot_longer(where(is.numeric)) %>%
filter(name %in% c("tot_cases", "tot_deaths")) %>%
group_by(state, name) %>%
summarize(value=sum(ifelse(src!="CDC daily (old)", value, 0)) - sum(ifelse(src=="CDC daily (old)", value, 0)),
.groups="drop"
) %>%
ggplot(aes(x=fct_reorder(state, value), y=value)) +
geom_point() +
facet_wrap(~name, scales="free_x") +
coord_flip() +
labs(x=NULL, y=NULL, title="Change in most recent burden by state (new minus old)") +
geom_hline(yintercept=0, lty=2)
At a first glance, national totals are well aligned between the existing daily data file and the new weekly data file. The newer data has two weeks of extra reporting, so most recent totals by state are generally slightly higher. The new data breaks apart NYC and NY, which need to be combined for processing the new file
Function readQCRawCDCDaily() is updated:
# Function to read and check a raw data file (last updated 16-NOV-2022, previously updated 02-AUG-2021)
readQCRawCDCDaily <- function(fileName,
writeLog=NULL,
ovrwriteLog=TRUE,
dfRef=NULL,
urlType=NULL,
url=NULL,
getData=TRUE,
ovrWriteDownload=FALSE,
vecRename=NULL,
selfList=NULL,
fullList=NULL,
uniqueBy=NULL,
step3Group=NULL,
step3Vals=NULL,
step4KeyVars=NULL,
step5PlotItems=NULL,
step6AggregateList=NULL,
inferVars=list("url"=urlMapper,
"vecRename"=renMapper,
"selfList"=selfListMapper,
"fullList"=fullListMapper,
"uniqueBy"=uqMapper,
"step3Group"=checkControlGroupMapper,
"step3Vals"=checkControlVarsMapper,
"step4KeyVars"=checkSimilarityMapper,
"step5PlotItems"=plotSimilarityMapper,
"step6AggregateList"=keyAggMapper
)
) {
# FUNCTION ARGUMENTS
# fileName: the location where downloaded data either is, or will be, stored
# writeLog: the external file location for printing (NULL means use the main log stdout)
# ovrwriteLog: boolean, if using an external log, should it be started from scratch (overwritten)?
# dfRef: a reference data frame for comparison (either NULL or NA means do not run comparisons)
# urlType: character vector that can be mapped using urlMapper and keyVarMapper
# url: direct URL passed as character string
# NOTE that if both url and urlType are NULL, no file will be downloaded
# getData: boolean, should an attempt be made to get new data using urlType or url?
# ovrWriteDownload: boolean, if fileName already exists, should it be overwritten?
# vecRename: vector for renaming c('existing name'='new name'), can be any length from 0 to ncol(df)
# NULL means infer from urlType, if not available there use c()
# selfList: list for functions to apply to self, list('variable'=fn) will apply variable=fn(variable)
# processed in order, so more than one function can be applied to self
# NULL means infer from urlType, if not available in mapping file use list()
# fullList: list for general functions to be applied, list('new variable'=expression(code))
# will create 'new variable' as eval(expression(code))
# for now, requires passing an expression
# NULL means infer from urlType, use list() if not in mapping file
# uniqueBy: combination of variables for checking uniqueness
# NULL means infer from data, keep as NULL (meaning use-all) if cannot be inferred
# step3Group: variable to be used as the x-axis (grouping) for step 3 plots
# NULL means infer from data
# step3Vals: values to be plotted on the y-axis for step 3 plots
# NULL means infer from data
# step4KeyVars: list of parameters to be passed as keyVars= in step 4
# NULL means infer from urlType
# step5PlotItems: items to be plotted in step 5
# NULL means infer from urlType
# step6AggregateList: drives the elements to be passed to compareAggregate() and flagLargeDelta()
# NULL means infer from urlType
# inferVars: vector of c('variable'='mapper') for inferring parameter values when passed as NULL
# Step 0a: Use urlType to infer key variables if passed as NULL
for (vrbl in names(inferVars)) {
mapper <- inferVars[[vrbl]]
if (is.null(get(vrbl))) {
if (urlType %in% names(mapper)) assign(vrbl, mapper[[urlType]])
else if ("default" %in% names(mapper)) assign(vrbl, mapper[["default"]])
}
}
# Step 1: Download a new file (if requested)
if (!is.null(url) & isTRUE(getData)) fileDownload(fileName=fileName, url=url, ovrWrite=ovrWriteDownload)
else cat("\nNo file has been downloaded, will use existing file:", fileName, "\n")
# Step 2: Read file, rename and mutate variables, confirm uniqueness by expected levels
dfRaw <- fileRead(fileName) %>%
colRenamer(vecRename) %>%
colMutater(selfList=selfList, fullList=fullList) %>%
checkUniqueRows(uniqueBy=uniqueBy)
# Step 3: Plot basic control totals for new cases and new deaths by month
dfRaw %>%
checkControl(groupBy=step3Group, useVars=step3Vals, printControls=FALSE, na.rm=TRUE) %>%
helperLinePlot(x=step3Group, y="newValue", facetVar="name", facetScales="free_y", groupColor="name")
# If there is no file for comparison, return the data
if (is.null(dfRef) | if(length(dfRef)==1) is.na(dfRef) else FALSE) return(dfRaw)
# Step 4b: Check similarity of existing and reference file
# ovrWriteLog=FALSE since everything should be an append after the opening text line in step 0
diffRaw <- checkSimilarity(df=dfRaw,
ref=dfRef,
keyVars=step4KeyVars,
writeLog=writeLog,
ovrwriteLog=FALSE
)
# Step 5: Plot the similarity checks
plotSimilarity(diffRaw, plotItems=step5PlotItems)
# Step 6: Plot and report on differences in aggregates
helperAggMap <- function(x) {
h1 <- compareAggregate(df=dfRaw, ref=dfRef, grpVar=x$grpVar, numVars=x$numVars,
sameUniverse=x$sameUniverse, plotData=x$plotData, isLine=x$isLine,
returnDelta=x$returnDelta)
if (isTRUE(x$flagLargeDelta)) {
h2 <- flagLargeDelta(h1, pctTol=x$pctTol, absTol=x$absTol, sortBy=x$sortBy,
dropNA=x$dropNA, printAll=x$printAll
)
if (is.null(writeLog)) print(h2)
else {
cat(nrow(h2), " records", sep="")
txt <- paste0("\n\n***Differences of at least ",
x$absTol,
" and at least ",
round(100*x$pctTol, 3), "%\n\n"
)
printLog(h2, txt=txt, writeLog=writeLog)
}
}
}
lapply(step6AggregateList, FUN=helperAggMap)
cat("\n\n")
# Return the raw data file
dfRaw
}
# Explore for only reading and returning new data
testFile <- "./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv"
dfTest <- readQCRawCDCDaily(fileName=testFile,
getData=FALSE,
urlType="cdcWeekly",
url=c(),
vecRename=c("end_date"="date"),
selfList=list(),
fullList=list(),
uniqueBy=c("state", "date"),
step3Group=c("date"),
step3Vals=c("new_cases", "new_deaths"),
step4KeyVars=list("date"=list("label"="date", "countOnly"=TRUE, "convChar"=TRUE),
"state"=list("label"="state", "countOnly"=FALSE)
), # will need to update checkSimilarityMapper
step5PlotItems=c("date"),
step6AggregateList=list() # will need to update keyAggMapper
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv## Rows: 8820 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (4): tot_cases, new_cases, tot_deaths, new_deaths
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
all.equal(dfTest, rename(tmpBurden, date=end_date))## [1] TRUEThe function for processing downloaded data, without comparison to previous data, works as intended. Next steps are to update the appropriate mapping files to include “cdcWeeklyBurden” as an option, then check that the function runs as intended. Elements are added to the mapping list manually at first:
# Need to combine NYC as part of NY
lstComboMapper$cdcWeeklyBurden <- list("nyc"=list("comboVar"="state",
"uqVars"="date",
"vecCombo"=c("NY"="NY", "NYC"="NY"),
"fn"=specNA(sum)
)
)
# Need to test URL mapping later
if("cdcWeeklyBurden" %in% names(urlMapper)) {
urlMapper["cdcWeeklyBurden"] <- "https://data.cdc.gov/api/views/pwn4-m3yp/rows.csv?accessType=DOWNLOAD"
} else {
origNames <- names(urlMapper)
urlMapper <- c(urlMapper, "https://data.cdc.gov/api/views/pwn4-m3yp/rows.csv?accessType=DOWNLOAD")
names(urlMapper) <- c(origNames, "cdcWeeklyBurden")
}
# Rename end_date to date
renMapper$cdcWeeklyBurden <- c('end_date'='date')
# No need for variable mapping (formats OK as-is)
selfListMapper$cdcWeeklyBurden <- list()
fullListMapper$cdcWeeklyBurden <- list()
# File should be unique by state-date
uqMapper$cdcWeeklyBurden <- c("state", "date")
# Keep only 50 states (after NYC mapping) and DC
lstFilterMapper$cdcWeeklyBurden <- list("state"=c(state.abb, "DC"))
# Keep date, state, tot_cases, new_cases, tot_deaths, new_deaths
vecSelectMapper$cdcWeeklyBurden <- c("date", "state", "tot_cases", "tot_deaths", "new_cases", "new_deaths")
# Checks for control groups
checkControlGroupMapper$cdcWeeklyBurden <- c("date")
checkControlVarsMapper$cdcWeeklyBurden <- c("new_cases", "new_deaths")
# Check for similarity mapping
checkSimilarityMapper$cdcWeeklyBurden <- list(date=list(label='date', countOnly=TRUE, convChar=TRUE),
state=list(label='state', countOnly=FALSE)
)
plotSimilarityMapper$cdcWeeklyBurden <- c("date")
# Update keyAggMapper (use cdcDaily for now, probably need to update later)
keyAggMapper$cdcWeeklyBurden <- keyAggMapper$cdcDaily
# No variables need to be kept as-is (avoiding rolling 7-day)
asIsMapper$cdcWeeklyBurden <- c()
# No changes needed to perCapMapper or hhsMapper
# Run sample code
dfTest_v2 <- readQCRawCDCDaily(fileName=testFile,
getData=FALSE,
urlType="cdcWeeklyBurden",
url=c()
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv## Rows: 8820 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (4): tot_cases, new_cases, tot_deaths, new_deaths
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
all.equal(dfTest, dfTest_v2)## [1] TRUENext steps are to enable URL downloads and enable checking against a reference file. Downloading of new data is attempted:
# Run sample code
dfTest_v3 <- readQCRawCDCDaily(fileName="./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv",
getData=TRUE,
urlType="cdcWeeklyBurden"
)## Rows: 8880 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
dfTest_v3## # A tibble: 8,880 × 10
## date_updated state start_date date tot_cases new_cases tot_de…¹ new_d…²
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-23 AK 2020-01-16 2020-01-22 0 0 0 0
## 2 2020-01-30 AK 2020-01-23 2020-01-29 0 0 0 0
## 3 2020-02-06 AK 2020-01-30 2020-02-05 0 0 0 0
## 4 2020-02-13 AK 2020-02-06 2020-02-12 0 0 0 0
## 5 2020-02-20 AK 2020-02-13 2020-02-19 0 0 0 0
## 6 2020-02-27 AK 2020-02-20 2020-02-26 0 0 0 0
## 7 2020-03-05 AK 2020-02-27 2020-03-04 0 0 0 0
## 8 2020-03-12 AK 2020-03-05 2020-03-11 0 0 0 0
## 9 2020-03-19 AK 2020-03-12 2020-03-18 11 11 0 0
## 10 2020-03-26 AK 2020-03-19 2020-03-25 63 52 1 1
## # … with 8,870 more rows, 2 more variables: new_historic_cases <dbl>,
## # new_historic_deaths <dbl>, and abbreviated variable names ¹tot_deaths,
## # ²new_deaths
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesChecking against a reference file is also enabled:
dfTest_v3_ref <- readQCRawCDCDaily(fileName="./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv",
getData=FALSE,
dfRef=dfTest_v2,
urlType="cdcWeeklyBurden"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv## Rows: 8880 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: new_historic_cases new_historic_deaths
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 1
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-26 tot_cases 71 83 12 0.15584416
## 2 2020-03-04 tot_cases 183 201 18 0.09375000
## 3 2020-03-11 tot_cases 1352 1427 75 0.05397625
## 4 2020-02-26 new_cases 24 31 7 0.25454545
## 5 2020-03-04 new_cases 112 118 6 0.05217391
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 FSM tot_cases 206651 206993 342 0.001653596
## 2 PA tot_cases 205201857 205436071 234214 0.001140732
## 3 CO new_deaths 13497 13473 24 0.001779755
## 4 OH new_deaths 40208 40249 41 0.001019178
## 5 FSM new_cases 22032 22203 171 0.007731434
## 6 KY new_cases 1622400 1619549 2851 0.001758819dfTest_v3_ref## # A tibble: 8,880 × 10
## date_updated state start_date date tot_cases new_cases tot_de…¹ new_d…²
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-23 AK 2020-01-16 2020-01-22 0 0 0 0
## 2 2020-01-30 AK 2020-01-23 2020-01-29 0 0 0 0
## 3 2020-02-06 AK 2020-01-30 2020-02-05 0 0 0 0
## 4 2020-02-13 AK 2020-02-06 2020-02-12 0 0 0 0
## 5 2020-02-20 AK 2020-02-13 2020-02-19 0 0 0 0
## 6 2020-02-27 AK 2020-02-20 2020-02-26 0 0 0 0
## 7 2020-03-05 AK 2020-02-27 2020-03-04 0 0 0 0
## 8 2020-03-12 AK 2020-03-05 2020-03-11 0 0 0 0
## 9 2020-03-19 AK 2020-03-12 2020-03-18 11 11 0 0
## 10 2020-03-26 AK 2020-03-19 2020-03-25 63 52 1 1
## # … with 8,870 more rows, 2 more variables: new_historic_cases <dbl>,
## # new_historic_deaths <dbl>, and abbreviated variable names ¹tot_deaths,
## # ²new_deaths
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesNew columns have been added to explain jumps in total cases/deaths that are not explained by that week’s reported new cases/deaths. These data are explored:
# Full file
testFull <- read_csv("./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv")## Rows: 8880 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Entries with non-zero for "new_historic_*"
testFull %>%
filter(new_historic_cases != 0 | new_historic_deaths != 0)## # A tibble: 5 × 10
## date_upd…¹ state start_date end_date tot_c…² new_c…³ tot_d…⁴ new_d…⁵ new_h…⁶
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2022-10-27 DC 2022-10-20 2022-10-26 169709 273 1392 -5 0
## 2 2022-11-10 DC 2022-11-03 2022-11-09 170482 441 1402 0 0
## 3 2022-09-29 KS 2022-09-22 2022-09-28 879001 2780 9554 7 0
## 4 2022-11-03 KS 2022-10-27 2022-11-02 890598 2388 9620 12 0
## 5 2022-10-27 NYC 2022-10-20 2022-10-26 2928439 14590 42863 91 0
## # … with 1 more variable: new_historic_deaths <dbl>, and abbreviated variable
## # names ¹date_updated, ²tot_cases, ³new_cases, ⁴tot_deaths, ⁵new_deaths,
## # ⁶new_historic_cases# Cases where cumsum and total are not equal
testFull %>%
arrange(state, end_date) %>%
group_by(state) %>%
mutate(delta=tot_deaths-cumsum(new_deaths)) %>%
filter(delta != 0)## # A tibble: 16 × 11
## # Groups: state [3]
## date_updated state start_date end_date tot_cases new_cases tot_de…¹ new_d…²
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
## 1 2022-10-27 DC 2022-10-20 2022-10-26 169709 273 1392 -5
## 2 2022-11-03 DC 2022-10-27 2022-11-02 170041 332 1397 5
## 3 2022-11-10 DC 2022-11-03 2022-11-09 170482 441 1402 0
## 4 2022-11-17 DC 2022-11-10 2022-11-16 170750 268 1403 1
## 5 2022-09-29 KS 2022-09-22 2022-09-28 879001 2780 9554 7
## 6 2022-10-06 KS 2022-09-29 2022-10-05 880633 1632 9573 19
## 7 2022-10-13 KS 2022-10-06 2022-10-12 883482 2849 9590 17
## 8 2022-10-20 KS 2022-10-13 2022-10-19 886123 2641 9601 11
## 9 2022-10-27 KS 2022-10-20 2022-10-26 888210 2087 9607 6
## 10 2022-11-03 KS 2022-10-27 2022-11-02 890598 2388 9620 12
## 11 2022-11-10 KS 2022-11-03 2022-11-09 893300 2702 9626 6
## 12 2022-11-17 KS 2022-11-10 2022-11-16 895776 2476 9652 26
## 13 2022-10-27 NYC 2022-10-20 2022-10-26 2928439 14590 42863 91
## 14 2022-11-03 NYC 2022-10-27 2022-11-02 2944270 15831 42880 17
## 15 2022-11-10 NYC 2022-11-03 2022-11-09 2962479 18209 42957 77
## 16 2022-11-17 NYC 2022-11-10 2022-11-16 2979932 17453 43033 76
## # … with 3 more variables: new_historic_cases <dbl>, new_historic_deaths <dbl>,
## # delta <dbl>, and abbreviated variable names ¹tot_deaths, ²new_deathsThe field appears to be working as intended. Next steps are to compare the cases and deaths data to the previous daily data files:
# Conversion of raw CDC daily data to cumsum
cdcDailyTest <- cdc_daily_221102$dfRaw$cdcDaily %>%
select(date, state, new_cases, new_deaths) %>%
arrange(state, date) %>%
group_by(state) %>%
mutate(cum_cases=cumsum(new_cases), cum_deaths=cumsum(new_deaths)) %>%
ungroup()
# Integration of new weekly data, using end_date=date
testCombo <- testFull %>%
select(date=end_date, state, wkly_tot_cases=tot_cases, wkly_tot_deaths=tot_deaths) %>%
left_join(cdcDailyTest, by=c("date", "state"))
testCombo## # A tibble: 8,880 × 8
## date state wkly_tot_cases wkly_tot_de…¹ new_c…² new_d…³ cum_c…⁴ cum_d…⁵
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-22 AK 0 0 0 0 0 0
## 2 2020-01-29 AK 0 0 0 0 0 0
## 3 2020-02-05 AK 0 0 0 0 0 0
## 4 2020-02-12 AK 0 0 0 0 0 0
## 5 2020-02-19 AK 0 0 0 0 0 0
## 6 2020-02-26 AK 0 0 0 0 0 0
## 7 2020-03-04 AK 0 0 0 0 0 0
## 8 2020-03-11 AK 0 0 0 0 0 0
## 9 2020-03-18 AK 11 0 5 0 8 1
## 10 2020-03-25 AK 63 1 7 0 49 1
## # … with 8,870 more rows, and abbreviated variable names ¹wkly_tot_deaths,
## # ²new_cases, ³new_deaths, ⁴cum_cases, ⁵cum_deaths# Calculation of RMSE by state for 'tot_deaths'
testCombo %>%
filter(date <= "2022-10-12") %>%
mutate(delta=wkly_tot_deaths-cum_deaths) %>%
group_by(state) %>%
summarize(rmse=sqrt(mean(delta**2)), r2=1-rmse/mean(cum_deaths)) %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
ggplot(aes(x=fct_reorder(state, r2), y=r2)) +
geom_col(fill="lightblue") +
geom_text(aes(label=round(r2, 2)), hjust=0) +
coord_flip() +
labs(x=NULL, y="Similarity of reported deaths (weekly vs. daily)\n(1.0 means perfect alignment)")
# Select states plotted
testCombo %>%
filter(state %in% c("OR", "LA", "DC", "PA", "NE", "WA", "OH", "MO", "AK")) %>%
select(date, state, wkly_tot_deaths, cum_deaths) %>%
pivot_longer(-c(state, date)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name,
color=c("wkly_tot_deaths"="Weekly data (new)", "cum_deaths"="Daily data (old)")[name]
)
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Source:") +
labs(title="Comparison of cumulative deaths by source", y="Cumulative deaths", x=NULL)## Warning: Removed 5 rows containing missing values (`geom_line()`).
# Overall data
testCombo %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(date) %>%
summarize(across(where(is.numeric), specNA())) %>%
select(date, wkly_tot_deaths, cum_deaths) %>%
pivot_longer(-c(date)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name,
color=c("wkly_tot_deaths"="Weekly data (new)", "cum_deaths"="Daily data (old)")[name]
)
) +
scale_color_discrete("Source:") +
labs(title="Comparison of cumulative deaths by source",
y="Cumulative deaths",
x=NULL,
subtitle="50 states plus DC"
)## Warning: Removed 5 rows containing missing values (`geom_line()`).
Alignment of reported deaths varies by state. In aggregate, the newer data appears to report slightly more deaths. The data file is processed using the existing function:
dfTestProcess <- processRawFile(dfTest_v3_ref,
vecRename=c(),
vecSelect=vecSelectMapper[["cdcWeeklyBurden"]],
lstCombo=lstComboMapper[["cdcWeeklyBurden"]],
lstFilter=lstFilterMapper[["cdcWeeklyBurden"]]
)##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 6.09e+9 8.48e+7 98174364 1072078 8732
## 2 after 6.04e+9 8.44e+7 97030505 1066086 7548
## 3 pctchg 7.56e-3 4.72e-3 0.0117 0.00559 0.136dfTestProcess## # A tibble: 7,548 × 6
## date state tot_cases tot_deaths new_cases new_deaths
## <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-22 AK 0 0 0 0
## 2 2020-01-29 AK 0 0 0 0
## 3 2020-02-05 AK 0 0 0 0
## 4 2020-02-12 AK 0 0 0 0
## 5 2020-02-19 AK 0 0 0 0
## 6 2020-02-26 AK 0 0 0 0
## 7 2020-03-04 AK 0 0 0 0
## 8 2020-03-11 AK 0 0 0 0
## 9 2020-03-18 AK 11 0 11 0
## 10 2020-03-25 AK 63 1 52 1
## # … with 7,538 more rowsData need to be converted appropriately to daily format for 7-day rolling averages. Totals should be carried forward, while new values should be left as-is. This step needs to be added either to processRawFile() or createPerCapita():
# Conversion to daily data
dfTestProcessDaily <- expand.grid(seq.Date(dfTestProcess %>% pull(date) %>% min,
dfTestProcess %>% pull(date) %>% max,
by=1
),
dfTestProcess %>% pull(state) %>% unique
) %>%
tibble::as_tibble() %>%
set_names(c("date", "state")) %>%
left_join(dfTestProcess, by=c("date", "state")) %>%
mutate(across(starts_with("tot"), .fns=function(x) zoo::na.locf(x)),
across(starts_with("new"), .fns=function(x) ifelse(is.na(x), 0, x))
)
dfTestProcessDaily## # A tibble: 52,530 × 6
## date state tot_cases tot_deaths new_cases new_deaths
## <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-22 AK 0 0 0 0
## 2 2020-01-23 AK 0 0 0 0
## 3 2020-01-24 AK 0 0 0 0
## 4 2020-01-25 AK 0 0 0 0
## 5 2020-01-26 AK 0 0 0 0
## 6 2020-01-27 AK 0 0 0 0
## 7 2020-01-28 AK 0 0 0 0
## 8 2020-01-29 AK 0 0 0 0
## 9 2020-01-30 AK 0 0 0 0
## 10 2020-01-31 AK 0 0 0 0
## # … with 52,520 more rowsdfTestPerCapita <- createPerCapita(dfTestProcessDaily,
uqBy=c("state", "date"),
popData=getStateData(),
mapper=perCapMapper,
asIsVars=if(isTRUE(exists("asIsMapper"))) asIsMapper[["cdcWeeklyBurden"]] else c()
)
dfTestPerCapita## # A tibble: 52,530 × 14
## date state tot_cases tot_deaths new_c…¹ new_d…² tcpm tdpm cpm dpm
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-22 AK 0 0 0 0 0 0 0 0
## 2 2020-01-22 AL 0 0 0 0 0 0 0 0
## 3 2020-01-22 AR 0 0 0 0 0 0 0 0
## 4 2020-01-22 AZ 0 0 0 0 0 0 0 0
## 5 2020-01-22 CA 0 0 0 0 0 0 0 0
## 6 2020-01-22 CO 0 0 0 0 0 0 0 0
## 7 2020-01-22 CT 0 0 0 0 0 0 0 0
## 8 2020-01-22 DC 0 0 0 0 0 0 0 0
## 9 2020-01-22 DE 5 0 5 0 5.13 0 5.13 0
## 10 2020-01-22 FL 0 0 0 0 0 0 0 0
## # … with 52,520 more rows, 4 more variables: tcpm7 <dbl>, tdpm7 <dbl>,
## # cpm7 <dbl>, dpm7 <dbl>, and abbreviated variable names ¹new_cases,
## # ²new_deathsA function is written for the conversion of weekly data to daily data:
convertWeeklyDaily <- function(df,
timeVar="date",
otherVars=c("state"),
timeUnitDays=1,
locfVars=c("tot_cases", "tot_deaths"),
naVars=c("new_cases", "new_deaths")
) {
# FUNCTION ARGUMENTS:
# df: the original daily frame
# timeVar: the time variable in the original dataset
# otherVars: other variables of importance (df should be unique by otherVars-timeVar)
# timeUnitDays: final data should be every 'timeUnitDays' days (e.g., 2 would be every other day)
# locfVars: variables for applying zoo::na.locf
# naVars: variables for applying ifelse(is.na(x), 0, x)
# Check that all variables are included
nms1 <- sort(names(df))
nms2 <- sort(c(timeVar, otherVars, locfVars, naVars))
if(!isTRUE(all.equal(nms1, nms2))) {
warning(paste0("\nSome variables not passed to convertWeeklyDaily() will be treated as-is:",
"\ndf has variables: ",
paste0(nms1, collapse=", "),
"\nvariables passed as arguments are: ",
paste0(nms2, collapse=", "),
"\n"
)
)
}
# Relevant times to include
keyTimes <- seq.Date(df %>% pull(timeVar) %>% min, df %>% pull(timeVar) %>% max, by=timeUnitDays) %>%
tibble::tibble() %>%
purrr::set_names(timeVar)
# Relevant levels to include
keyLevels <- df %>% select(all_of(otherVars)) %>% unique()
# Return cross-join tibble
tibble::tibble(keyTimes) %>%
full_join(keyLevels, by=character()) %>%
purrr::set_names(c(timeVar, otherVars)) %>%
left_join(df, by=c(timeVar, otherVars)) %>%
arrange(across(c(all_of(otherVars), all_of(timeVar)))) %>%
group_by(across(all_of(otherVars))) %>%
mutate(across(all_of(locfVars), .fns=function(x) zoo::na.locf(x)),
across(all_of(naVars), .fns=function(x) ifelse(is.na(x), 0, x))
) %>%
ungroup()
}
all.equal(dfTestProcessDaily %>% arrange(state, date), convertWeeklyDaily(dfTestProcess), check.attributes=FALSE)## [1] TRUEThe diagnostic functions are also run:
# Create plot data
plotDataTest <- diagnoseClusters(lst=list("stateData"=getStateData(),
"dfPerCapita"=dfTestPerCapita %>% mutate(hpm7=0),
"useClusters"=readFromRDS("cdc_daily_210528")$useClusters
),
lstExtract=list("stateData"=function(x) colSelector(x, vecSelect=c("state", "pop")),
"dfPerCapita"=NULL
),
wm_aggVars=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7"),
detailAggVars=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7"),
brewPalette="Paired"
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
The missing hospital data need to be located and/or processing updated. Otherwise, existing functionality appears to work on weekly data converted to daily data. These should integrate naturally if included in processing, as hospital and vaccine data are still available from existing sources. Function readRunCDCDaily() is updated for conversions:
# Function to download/load, process, segment, and analyze data for CDC daily (last updated 02-AUG-2021)
readRunCDCDaily <- function(thruLabel,
downloadTo=list("cdcDaily"=NA, "cdcHosp"=NA, "vax"=NA),
readFrom=downloadTo,
compareFile=list("cdcDaily"=NA, "cdcHosp"=NA, "vax"=NA),
writeLog=NULL,
ovrwriteLog=TRUE,
dfPerCapita=NULL,
useClusters=NULL,
hierarchical=TRUE,
returnList=!isTRUE(hierarchical),
kCut=6,
reAssignState=vector("list", 0),
weightedMeanAggs=eval(formals(diagnoseClusters)$wm_aggVars),
detailedPlotAggs=weightedMeanAggs,
skipAssessmentPlots=FALSE,
brewPalette=NA,
convertWeekly=c("cdcWeeklyBurden"),
...
) {
# FUNCTION ARGUMENTS:
# thruLabel: the label for when the data are through (e.g., "Aug 30, 2020")
# donwloadTo: named list for locations to download data (cdcDaily and cdcHosp)
# NA means do not download data for that particular element
# readFrom: named list for locations to read data from (defaults to donwloadTo)
# compareFile: named list for the reference file to be used for cdcDaily and cdcHosp
# NA means do not use a reference file for that element
# dateChangePlot: boolean, should changes in dates be captured as a plot rather than as a list?
# dateMetricPrint: boolean, should the changes by date and metric be printed to the main log?
# writeLog: name of a separate log file for capturing detailed data on changes between files
# NULL means no detailed data captured
# ovrwriteLog: boolean, should the log file be overwritten and started again from scratch?
# dfPerCapita: file can be passed directly, which bypasses the loading and processing steps
# default NULL means create dfPerCapita using steps 2-4
# useClusters: file containing clusters by state (NULL means make the clusters from the data)
# hierarchical: boolean, should hierarchical clusters be produced (if FALSE, will be k-means)?
# returnList: boolean, should a list be returned or just the cluster object?
# refers to what is returned by clusterStates(); the main function always returns a list
# kCut: number of segments when cutting the hierarchical tree
# reAssignState: mapping file for assigning a state to another state's cluster
# format list("stateToChange"="stateClusterToAssign")
# weightedMeanAggs: variables where a population-weighted cluster mean should be created
# detailedPlotAggs: variables that should be included in the cluster-level disease evolution detailed plots
# skipAssessmentPlots: boolean to skip the plots for assessClusters()
# especially useful if just exploring dendrograms or silhouette widths
# brewPalette: create plots using this color scheme (needs to be valid in ggplot2::scale_*_brewer())
# NA means use R default color schemes
# convertWeekly: signals that dfProcess for this data frame should be passed through convertWeeklyDaily()
# ...: arguments to be passed to clusterStates(), will be used only if useClusters is NULL
# STEP 0: Function to create the return list
createFinalList <- function(plots=TRUE) {
list(stateData=stateData,
dfRaw=dfRawList,
dfProcess=dfProcessList,
dfPerCapita=dfPerCapita,
useClusters=useClusters,
plotDataList=if(plots) plotDataList else NULL
)
}
# STEP 1: Get state data
stateData <- getStateData()
# If a log file is requested, create the log file (allows for append=TRUE for all downstream functions)
if (!is.null(writeLog)) genNewLog(writeLog=writeLog, ovrwriteLog=ovrwriteLog)
# Get the data types to be used (will be the elements of readFrom) and create a file storage list
elemUsed <- names(readFrom)
dfRawList <- vector("list", length=length(elemUsed)) %>% purrr::set_names(elemUsed)
dfProcessList <- vector("list", length=length(elemUsed)) %>% purrr::set_names(elemUsed)
# Steps 2-4 are run only is dfPerCapita has not been passed
if (is.null(dfPerCapita)) {
# Step 2: Download and QC all of the requested data
for (elem in elemUsed) {
dfRawList[[elem]] <- readQCRawCDCDaily(fileName=readFrom[[elem]],
writeLog=writeLog,
ovrwriteLog=FALSE,
urlType=elem,
getData=if(is.na(downloadTo[[elem]])) FALSE else TRUE,
dfRef=compareFile[[elem]]
)
glimpseLog(dfRawList[[elem]], txt=paste0("\nRaw file for ", elem, ":\n"), logFile=writeLog)
}
# Step 3: Process all of the requested data
for (elem in elemUsed) {
dfProcessList[[elem]] <- processRawFile(dfRawList[[elem]],
vecRename=c(), # already handled in readQCRawCDCDaily()
vecSelect=vecSelectMapper[[elem]],
lstCombo=lstComboMapper[[elem]],
lstFilter=lstFilterMapper[[elem]]
)
# Run conversions of weekly to daily if needed
if(elem %in% all_of(convertWeekly)) dfProcessList[[elem]] <- convertWeeklyDaily(dfProcessList[[elem]])
glimpseLog(dfProcessList[[elem]], txt=paste0("\nProcessed for ", elem, ":\n"), logFile=writeLog)
}
# Step 4: Integrate in to a dfPerCapita file and glimpse (to specified log file)
dfPerCapita <- createPerCapita(dfProcessList,
uqBy=c("state", "date"),
popData=stateData,
mapper=perCapMapper,
asIsVars=if(isTRUE(exists("asIsMapper"))) unname(unlist(asIsMapper)) else c()
)
glimpseLog(dfPerCapita, txt="\nIntegrated per capita data file:\n", logFile=writeLog)
} else {
dfRawList <- NULL
dfProcessList <- NULL
}
# STEP 5: Create the clusters (if they have not been passed)
if (is.null(useClusters)) {
clData <- clusterStates(df=dfPerCapita, hierarchical=hierarchical, returnList=returnList, ...)
useClusters <- getClusters(clData, hier=hierarchical, kCut=kCut, reAssign=reAssignState)
}
# STEP 5a: Stop the process and return what is available if skipAssessmentPlots is TRUE
if (skipAssessmentPlots)
return(createFinalList(plots=FALSE))
# STEP 6: Create the cluster assessments
lstFuns <- list("stateData"=function(x) colSelector(x, vecSelect=c("state", "pop")),
"dfPerCapita"=NULL
)
plotDataList <- diagnoseClusters(lst=list("stateData"=stateData,
"dfPerCapita"=dfPerCapita,
"useClusters"=useClusters
),
lstExtract=lstFuns,
wm_aggVars=weightedMeanAggs,
detailAggVars=detailedPlotAggs,
brewPalette=brewPalette
)
# STEP 7: Return a list of the key data
return(createFinalList(plots=TRUE))
}Next steps are to test the function on all data sources at the same time. The diagnostic functions are first run using an amalgamated dfProcess:
# Create per-capita data
asIsMapper <- list("cdcDaily"=c(),
"cdcWeekly"=c(),
"cdcHosp"=c(),
"vax"=c("vxcpoppct", "vxcgte65", "vxcgte65pct", "vxcgte18", "vxcgte18pct")
)
dfTestPerCapita_v2 <- createPerCapita(list(cdcDaily=dfTestProcessDaily,
cdcHosp=cdc_daily_221102$dfProcess$cdcHosp,
vax=cdc_daily_221102$dfProcess$vax
),
uqBy=c("state", "date"),
popData=getStateData(),
mapper=perCapMapper,
asIsVars=if(isTRUE(exists("asIsMapper"))) unname(unlist(asIsMapper)) else c()
)
dfTestPerCapita_v2## # A tibble: 52,692 × 34
## date state tot_ca…¹ tot_d…² new_c…³ new_d…⁴ inp hosp_…⁵ hosp_…⁶ vxa
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-01 AL NA NA NA NA NA NA NA NA
## 2 2020-01-01 HI NA NA NA NA 0 NA NA NA
## 3 2020-01-01 IN NA NA NA NA 0 NA NA NA
## 4 2020-01-01 LA NA NA NA NA NA NA NA NA
## 5 2020-01-01 MN NA NA NA NA 0 NA NA NA
## 6 2020-01-01 MT NA NA NA NA 0 NA NA NA
## 7 2020-01-01 NC NA NA NA NA 0 NA NA NA
## 8 2020-01-01 TX NA NA NA NA 0 NA NA NA
## 9 2020-01-02 AL NA NA NA NA NA NA NA NA
## 10 2020-01-02 HI NA NA NA NA 0 NA NA NA
## # … with 52,682 more rows, 24 more variables: vxc <dbl>, vxcpoppct <dbl>,
## # vxcgte65 <dbl>, vxcgte65pct <dbl>, vxcgte18 <dbl>, vxcgte18pct <dbl>,
## # tcpm <dbl>, tdpm <dbl>, cpm <dbl>, dpm <dbl>, hpm <dbl>, ahpm <dbl>,
## # phpm <dbl>, vxapm <dbl>, vxcpm <dbl>, tcpm7 <dbl>, tdpm7 <dbl>, cpm7 <dbl>,
## # dpm7 <dbl>, hpm7 <dbl>, ahpm7 <dbl>, phpm7 <dbl>, vxapm7 <dbl>,
## # vxcpm7 <dbl>, and abbreviated variable names ¹tot_cases, ²tot_deaths,
## # ³new_cases, ⁴new_deaths, ⁵hosp_adult, ⁶hosp_pedall.equal(dfTestPerCapita_v2 %>%
select(names(dfTestPerCapita)) %>%
filter(date >= "2020-01-22", date <= "2022-11-16"),
dfTestPerCapita
)## [1] TRUE# Create plot data
plotDataTest_v2 <- diagnoseClusters(lst=list("stateData"=getStateData(),
"dfPerCapita"=dfTestPerCapita_v2,
"useClusters"=readFromRDS("cdc_daily_210528")$useClusters
),
lstExtract=list("stateData"=function(x) colSelector(x, vecSelect=c("state", "pop")),
"dfPerCapita"=NULL
),
wm_aggVars=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7"),
detailAggVars=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7"),
brewPalette="Paired"
)
Data appear to be integrating as expected. The asIsMapper call in readRunCDCDaily() has also been updated to correctly call every item in a vector in the list
The function is run to get the most recent weekly data and integrate with existing hospital and vaccines data:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_221128.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221102.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221102.csv"
)
compareList <- list("cdcWeeklyBurden"=dfTest_v3_ref,
"cdcHosp"=readFromRDS("cdc_daily_221002")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_221002")$dfRaw$vax
)
cdc_daily_221128 <- readRunCDCDaily(thruLabel="Nov 24, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 8940 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 1
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-05-18 new_deaths 2263 4129 1866 0.5838548
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 IL tot_deaths 3393067 3354725 38342 0.011364310
## 2 FSM tot_deaths 599 605 6 0.009966777
## 3 MP tot_deaths 1776 1772 4 0.002254791
## 4 FSM new_deaths 55 58 3 0.053097345
## 5 CO new_deaths 13555 13506 49 0.003621448
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 8,940
## Columns: 10
## $ date_updated <date> 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-13, 2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <date> 2020-01-16, 2020-01-23, 2020-01-30, 2020-02-06, 2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_221102.csv## Rows: 52391 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 6791 4561 2230 0.3928823
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NV inp 669396 670631 1235 0.001843246
## 2 NH hosp_ped 1328 1282 46 0.035249042
## 3 WV hosp_ped 6407 6206 201 0.031871878
## 4 ME hosp_ped 2722 2675 47 0.017417084
## 5 MA hosp_ped 14134 13890 244 0.017413645
## 6 NV hosp_ped 5928 5831 97 0.016498002
## 7 VI hosp_ped 128 130 2 0.015503876
## 8 SC hosp_ped 10059 9955 104 0.010392725
## 9 AR hosp_ped 14079 13965 114 0.008130081
## 10 AZ hosp_ped 29543 29312 231 0.007849800
## 11 TN hosp_ped 24231 24060 171 0.007082065
## 12 AL hosp_ped 22626 22485 141 0.006251247
## 13 VT hosp_ped 658 654 4 0.006097561
## 14 UT hosp_ped 11316 11255 61 0.005405166
## 15 NM hosp_ped 8584 8545 39 0.004553681
## 16 IN hosp_ped 18602 18682 80 0.004291385
## 17 KS hosp_ped 5308 5288 20 0.003775009
## 18 PR hosp_ped 25141 25055 86 0.003426568
## 19 CO hosp_ped 23478 23402 76 0.003242321
## 20 FL hosp_ped 97891 97593 298 0.003048843
## 21 MS hosp_ped 12979 13017 38 0.002923527
## 22 KY hosp_ped 22910 22845 65 0.002841220
## 23 NJ hosp_ped 20956 20999 43 0.002049815
## 24 IA hosp_ped 8413 8430 17 0.002018643
## 25 PA hosp_ped 59345 59227 118 0.001990352
## 26 GA hosp_ped 57694 57605 89 0.001543812
## 27 OK hosp_ped 28563 28528 35 0.001226113
## 28 MO hosp_ped 43011 43059 48 0.001115371
## 29 IL hosp_ped 47538 47488 50 0.001052344
## 30 OR hosp_ped 12762 12749 13 0.001019168
## 31 NV hosp_adult 616917 618249 1332 0.002156795
## 32 WV hosp_adult 345523 345909 386 0.001116523
##
##
##
## Raw file for cdcHosp:
## Rows: 52,391
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_221102.csv## Warning: One or more parsing issues, see `problems()` for details## Rows: 36696 Columns: 101
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## lgl (5): Administered_Bivalent, Admin_Bivalent_PFR, Admin_Bivalent_MOD, Dis...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,696
## Columns: 101
## $ date <date> 2022-10-26, 2022-10-26, 2022-1…
## $ MMWR_week <dbl> 43, 43, 43, 43, 43, 43, 43, 43,…
## $ state <chr> "CO", "BP2", "DD2", "AR", "LA",…
## $ Distributed <dbl> 15839015, 389930, 7382310, 7633…
## $ Distributed_Janssen <dbl> 500100, 16200, 210700, 262300, …
## $ Distributed_Moderna <dbl> 5607840, 171620, 2394540, 31571…
## $ Distributed_Pfizer <dbl> 9713375, 202110, 4748270, 41899…
## $ Distributed_Novavax <dbl> 17700, 0, 28800, 23800, 6600, 4…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 275043, 0, 0, 252938, 206691, 2…
## $ Distributed_Per_100k_5Plus <dbl> 291881, 0, 0, 269787, 221024, 2…
## $ Distributed_Per_100k_12Plus <dbl> 320884, 0, 0, 298492, 244911, 2…
## $ Distributed_Per_100k_18Plus <dbl> 352039, 0, 0, 329350, 269818, 2…
## $ Distributed_Per_100k_65Plus <dbl> 1880200, 0, 0, 1457040, 1296680…
## $ vxa <dbl> 12152626, 335497, 9079453, 4641…
## $ Administered_5Plus <dbl> 12068391, 335495, 9063024, 4627…
## $ Administered_12Plus <dbl> 11620758, 335495, 8883529, 4499…
## $ Administered_18Plus <dbl> 10883886, 335495, 8387809, 4225…
## $ Administered_65Plus <dbl> 2933378, 11277, 857771, 1405163…
## $ Administered_Janssen <dbl> 345410, 14176, 327958, 125793, …
## $ Administered_Moderna <dbl> 4622790, 154826, 2958844, 19766…
## $ Administered_Pfizer <dbl> 7165826, 166463, 5744807, 25343…
## $ Administered_Novavax <dbl> 1712, 0, 2484, 268, 480, 295, 2…
## $ Administered_Unk_Manuf <dbl> 16888, 32, 45360, 4366, 3639, 5…
## $ Admin_Per_100k <dbl> 211029, 0, 0, 153800, 144276, 1…
## $ Admin_Per_100k_5Plus <dbl> 222396, 0, 0, 163556, 154002, 1…
## $ Admin_Per_100k_12Plus <dbl> 235426, 0, 0, 175954, 167022, 1…
## $ Admin_Per_100k_18Plus <dbl> 241906, 0, 0, 182340, 174300, 2…
## $ Admin_Per_100k_65Plus <dbl> 348212, 0, 0, 268221, 266760, 2…
## $ Recip_Administered <dbl> 12178883, 335497, 9079453, 4695…
## $ Administered_Dose1_Recip <dbl> 4745768, 152116, 4600027, 20805…
## $ Administered_Dose1_Pop_Pct <dbl> 82.4, 0.0, 0.0, 68.9, 62.4, 77.…
## $ Administered_Dose1_Recip_5Plus <dbl> 4700168, 152114, 4589610, 20718…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 86.6, 0.0, 0.0, 73.2, 66.5, 82.…
## $ Administered_Dose1_Recip_12Plus <dbl> 4480951, 152114, 4486081, 20017…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 90.8, 0.0, 0.0, 78.3, 71.7, 88.…
## $ Administered_Dose1_Recip_18Plus <dbl> 4155669, 152114, 4230192, 18630…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 92.4, 0.0, 0.0, 80.4, 74.0, 90.…
## $ Administered_Dose1_Recip_65Plus <dbl> 890491, 4115, 476135, 515480, 6…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 0.0, 0.0, 95.0, 92.3, 95.…
## $ vxc <dbl> 4175718, 138953, 3655064, 16955…
## $ vxcpoppct <dbl> 72.5, 0.0, 0.0, 56.2, 54.7, 62.…
## $ Series_Complete_5Plus <dbl> 4156753, 138952, 3652831, 16926…
## $ Series_Complete_5PlusPop_Pct <dbl> 76.6, 0.0, 0.0, 59.8, 58.4, 66.…
## $ Series_Complete_12Plus <dbl> 3972620, 138952, 3583964, 16411…
## $ Series_Complete_12PlusPop_Pct <dbl> 80.5, 0.0, 0.0, 64.2, 63.2, 71.…
## $ vxcgte18 <dbl> 3681706, 138952, 3383916, 15285…
## $ vxcgte18pct <dbl> 81.8, 0.0, 0.0, 66.0, 65.6, 73.…
## $ vxcgte65 <dbl> 808256, 3763, 251475, 435109, 6…
## $ vxcgte65pct <dbl> 95.0, 0.0, 0.0, 83.1, 86.9, 87.…
## $ Series_Complete_Janssen <dbl> 316583, 13495, 319863, 115591, …
## $ Series_Complete_Moderna <dbl> 1482213, 56214, 1106217, 684277…
## $ Series_Complete_Pfizer <dbl> 2364703, 69244, 2216621, 893449…
## $ Series_Complete_Novavax <dbl> 593, 0, 931, 150, 147, 104, 130…
## $ Series_Complete_Unk_Manuf <dbl> 4720, 0, 10757, 1304, 1348, 186…
## $ Series_Complete_Janssen_5Plus <dbl> 316563, 13494, 319842, 115470, …
## $ Series_Complete_Moderna_5Plus <dbl> 1470443, 56214, 1104733, 683178…
## $ Series_Complete_Pfizer_5Plus <dbl> 2364457, 69244, 2216589, 892657…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 4699, 0, 10736, 1295, 1343, 186…
## $ Series_Complete_Janssen_12Plus <dbl> 316553, 13494, 319830, 115462, …
## $ Series_Complete_Moderna_12Plus <dbl> 1469786, 56214, 1104614, 682949…
## $ Series_Complete_Pfizer_12Plus <dbl> 2181121, 69244, 2148140, 841426…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 4569, 0, 10450, 1248, 1306, 183…
## $ Series_Complete_Janssen_18Plus <dbl> 316207, 13494, 319494, 115013, …
## $ Series_Complete_Moderna_18Plus <dbl> 1469043, 56214, 1103520, 681652…
## $ Series_Complete_Pfizer_18Plus <dbl> 1892075, 69244, 1950664, 730710…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 3801, 0, 9316, 1173, 1259, 176,…
## $ Series_Complete_Janssen_65Plus <dbl> 29249, 196, 10615, 15940, 22522…
## $ Series_Complete_Moderna_65Plus <dbl> 383852, 1826, 84801, 243252, 30…
## $ Series_Complete_Pfizer_65Plus <dbl> 393887, 1741, 155484, 175349, 3…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 1189, 0, 563, 564, 219, 63, 878…
## $ Additional_Doses <dbl> 2342627, 52153, 966851, 730243,…
## $ Additional_Doses_Vax_Pct <dbl> 56.1, 37.5, 26.5, 43.1, 42.3, 4…
## $ Additional_Doses_5Plus <dbl> 2342527, 52153, 966774, 730158,…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 56.4, 37.5, 26.5, 43.1, 42.3, 4…
## $ Additional_Doses_12Plus <dbl> 2300475, 52153, 959937, 723815,…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 57.9, 37.5, 26.8, 44.1, 43.1, 4…
## $ Additional_Doses_18Plus <dbl> 2188216, 52153, 921716, 700345,…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 59.4, 37.5, 27.2, 45.8, 44.8, 4…
## $ Additional_Doses_50Plus <dbl> 1227186, 16410, 283327, 492070,…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 72.5, 54.6, 38.0, 58.5, 58.2, 5…
## $ Additional_Doses_65Plus <dbl> 645598, 2658, 100062, 298265, 4…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 79.9, 70.6, 39.8, 68.5, 68.3, 6…
## $ Additional_Doses_Moderna <dbl> 989679, 31561, 327343, 345696, …
## $ Additional_Doses_Pfizer <dbl> 1323953, 19927, 628820, 373658,…
## $ Additional_Doses_Janssen <dbl> 27100, 643, 6220, 10264, 13964,…
## $ Additional_Doses_Unk_Manuf <dbl> 1855, 22, 4183, 611, 260, 55, 3…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 652497, 3571, 74708, 190165, 24…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 53.2, 21.8, 26.4, 38.6, 32.1, 3…
## $ Second_Booster_65Plus <dbl> 400749, 866, 33346, 135359, 166…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 62.1, 32.6, 33.3, 45.4, 37.9, 4…
## $ Second_Booster_Janssen <dbl> 421, 3, 94, 116, 168, 53, 313, …
## $ Second_Booster_Moderna <dbl> 325593, 2532, 30870, 104368, 11…
## $ Second_Booster_Pfizer <dbl> 493405, 2152, 74296, 108959, 15…
## $ Second_Booster_Unk_Manuf <dbl> 742, 2, 595, 149, 38, 17, 67, 0…
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 6.19e+9 8.59e+7 98481551 1074742 8791
## 2 after 6.14e+9 8.55e+7 97329491 1068720 7599
## 3 pctchg 7.62e-3 4.73e-3 0.0117 0.00560 0.136
##
##
## Processed for cdcWeeklyBurden:
## Rows: 52,887
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.27e+7 4.60e+7 1311468 52391
## 2 after 5.25e+7 4.58e+7 1285571 50037
## 3 pctchg 5.43e-3 5.19e-3 0.0197 0.0449
##
##
## Processed for cdcHosp:
## Rows: 50,037
## Columns: 5
## $ date <date> 2021-01-26, 2021-01-23, 2021-01-21, 2021-01-13, 2021-01-10…
## $ state <chr> "OK", "MN", "RI", "OR", "RI", "KS", "SC", "RI", "RI", "LA",…
## $ inp <dbl> 1598, 618, 407, 512, 449, 989, 2246, 461, 483, 1619, 471, 7…
## $ hosp_adult <dbl> 1516, 598, 399, 507, 446, 986, 2232, 456, 479, 1609, 469, 7…
## $ hosp_ped <dbl> 82, 20, 8, 5, 3, 3, 14, 5, 4, 10, 2, 81, 16, 1, 9, 22, 1, 0…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.24e+11 1.74e+11 1544457. 4.40e+10 2.28e+6 1.61e+11 1.82e+6 3.67e+4
## 2 after 2.04e+11 8.40e+10 1292563. 2.13e+10 2.02e+6 7.77e+10 1.54e+6 2.90e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.15e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 29,019
## Columns: 9
## $ date <date> 2022-10-26, 2022-10-26, 2022-10-26, 2022-10-26, 2022-10-2…
## $ state <chr> "CO", "AR", "LA", "NV", "KY", "MN", "AL", "OH", "MT", "WV"…
## $ vxa <dbl> 12152626, 4641392, 6707088, 5315414, 7144109, 11838868, 66…
## $ vxc <dbl> 4175718, 1695580, 2541814, 1930260, 2634385, 4013235, 2576…
## $ vxcpoppct <dbl> 72.5, 56.2, 54.7, 62.7, 59.0, 71.2, 52.5, 59.8, 58.4, 59.2…
## $ vxcgte65 <dbl> 808256, 435109, 644253, 434579, 663968, 908701, 714593, 18…
## $ vxcgte65pct <dbl> 95.0, 83.1, 86.9, 87.6, 88.5, 95.0, 84.1, 88.9, 88.3, 86.8…
## $ vxcgte18 <dbl> 3681706, 1528584, 2334821, 1747560, 2393411, 3494198, 2386…
## $ vxcgte18pct <dbl> 81.8, 66.0, 65.6, 73.2, 69.1, 80.6, 62.6, 69.2, 67.6, 68.4…
##
## Integrated per capita data file:
## Rows: 53,049
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
saveToRDS(cdc_daily_221128, ovrWriteError=FALSE)Data are checked for consistency with previous data:
tempGetBurden <- function(df,
numVars=c("tcpm7", "cpm7", "tdpm7", "dpm7"),
natPop=getStateData() %>% pull(pop) %>% sum()
) {
df %>%
select(c("state", "date", all_of(numVars))) %>%
pivot_longer(-c(state, date)) %>%
filter(!is.na(value)) %>%
left_join(getStateData() %>% select(state, pop), by="state") %>%
group_by(date, name) %>%
summarize(natPerCap=sum(value*pop)/natPop, .groups="drop")
}
# Plot for new weekly data
tempGetBurden(cdc_daily_221128$dfPerCapita) %>%
ggplot(aes(x=date, y=natPerCap)) +
geom_line() +
facet_wrap(~name, scales="free_y") +
labs(x=NULL, y="Rolling 7 per million", title="Weekly data per million (rolling 7-day average)")
# Plot for old daily data
tempGetBurden(cdc_daily_221102$dfPerCapita) %>%
ggplot(aes(x=date, y=natPerCap)) +
geom_line() +
facet_wrap(~name, scales="free_y") +
labs(x=NULL, y="Rolling 7 per million", title="Daily data per million (rolling 7-day average)")
# Integrated data
tempGetBurden(cdc_daily_221128$dfPerCapita) %>%
bind_rows(tempGetBurden(cdc_daily_221102$dfPerCapita), .id="src") %>%
mutate(src=c("1"="New Weekly", "2"="Old Daily")[src]) %>%
ggplot(aes(x=date, y=natPerCap)) +
geom_line(aes(color=src, group=src)) +
facet_wrap(~name, scales="free_y") +
labs(x=NULL,
y="Rolling 7 per million",
title="Data per million comparison of new weekly and old daily (rolling 7-day average)"
)
As expected, there are minor differences in rolling-7 totals by day for data reported on a daily and weekly basis. Overall, trends and data volumes are very similar, at a national level, between the data sources. Weekly data could be shifted “backwards” by a few days to reflect that a spike of 5 deaths per day will report as …0-0-0-0-0-0-35-0-0…, which converts to rolling-7 as 0-0-0-5-5-5-5-5-5-5… Given the many issues in the data capture and reporting, this likely has minor, if any, practical impact
Comparisons are also made by state and metric:
dfSimilar <- cdc_daily_221102$dfPerCapita %>%
select(c("state", "date", where(is.numeric))) %>%
pivot_longer(-c(state, date)) %>%
filter(!is.na(value)) %>%
bind_rows(cdc_daily_221128$dfPerCapita %>%
select(c("state", "date", where(is.numeric))) %>%
pivot_longer(-c(state, date)) %>%
filter(!is.na(value)),
.id="src"
) %>%
mutate(src=c("1"="Old Daily", "2"="New Weekly")[src]) %>%
filter(name %in% c("tcpm7", "tdpm7", "cpm7", "dpm7"))
dfSimilar## # A tibble: 413,304 × 5
## src state date name value
## <chr> <chr> <date> <chr> <dbl>
## 1 Old Daily AK 2020-01-25 tcpm7 0
## 2 Old Daily AK 2020-01-25 tdpm7 0
## 3 Old Daily AK 2020-01-25 cpm7 0
## 4 Old Daily AK 2020-01-25 dpm7 0
## 5 Old Daily AL 2020-01-25 tcpm7 0
## 6 Old Daily AL 2020-01-25 tdpm7 0
## 7 Old Daily AL 2020-01-25 cpm7 0
## 8 Old Daily AL 2020-01-25 dpm7 0
## 9 Old Daily AR 2020-01-25 tcpm7 0
## 10 Old Daily AR 2020-01-25 tdpm7 0
## # … with 413,294 more rows# Ratio comparisons
dfSimilar %>%
filter(date >= "2020-03-01", date <= "2022-10-15") %>%
pivot_wider(c(state, date, name), names_from="src", values_fill=0) %>%
group_by(state, name) %>%
summarize(meanDelta=sqrt(mean((`Old Daily`-`New Weekly`)**2)),
meanDaily=mean(`Old Daily`),
meanWeekly=mean(`New Weekly`),
.groups="drop"
) %>%
mutate(keyMetric=meanDelta*2/(meanDaily+meanWeekly)) %>%
ggplot(aes(x=fct_reorder(state, keyMetric), y=keyMetric)) +
geom_col(fill="lightblue") +
geom_text(aes(label=round(keyMetric, 2))) +
coord_flip() +
facet_wrap(~name, scales="free_x", nrow=1) +
labs(x=NULL, y="RMSE divided by mean", title="Difference in weekly and daily data by state")
# Plotting data for Maine (high RMSE in cpm7)
dfSimilar %>%
filter(state=="ME") %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name, scales="free_y") +
labs(title="Example data for Maine", x=NULL, y="Burden per million (rolling 7-day average)")
# Plotting data for Washington (high RMSE in tdpm7)
dfSimilar %>%
filter(state=="WA") %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name, scales="free_y") +
labs(title="Example data for Washington", x=NULL, y="Burden per million (rolling 7-day average)")
At a state level, there are some differences in timing of waves between the daily and weekly data. Washington is particularly anomalous in having spikes in deaths offset by several weeks or months, even as cases are well aligned.
The latest data are downloaded and processed:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_221202.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221202.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221202.csv"
)
compareList <- list("cdcWeeklyBurden"=dfTest_v3_ref,
"cdcHosp"=readFromRDS("cdc_daily_221102")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_221102")$dfRaw$vax
)
cdc_daily_221202 <- readRunCDCDaily(thruLabel="Nov 30, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 9000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 2
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-05-18 new_deaths 2268 4129 1861 0.5818352
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 IL tot_deaths 3393067 3354725 38342 0.011364310
## 2 FSM tot_deaths 599 605 6 0.009966777
## 3 NY tot_deaths 2690681 2708899 18218 0.006747932
## 4 MP tot_deaths 1776 1772 4 0.002254791
## 5 DE tot_deaths 257554 257087 467 0.001814857
## 6 DE tot_cases 19368447 19422200 53753 0.002771441
## 7 NY tot_cases 204316373 204543867 227494 0.001112820
## 8 FSM new_deaths 55 58 3 0.053097345
## 9 NY new_deaths 30317 30541 224 0.007361399
## 10 CO new_deaths 13555 13506 49 0.003621448
## 11 DE new_deaths 3172 3163 9 0.002841358
## 12 NY new_cases 3331848 3340730 8882 0.002662239
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 9,000
## Columns: 10
## $ date_updated <date> 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-13, 2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <date> 2020-01-16, 2020-01-23, 2020-01-30, 2020-02-06, 2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…## Rows: 54011 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 30
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 5318 6791 1473 0.2432901
## 2 2020-07-25 hosp_ped 5434 4508 926 0.1862804
## 3 2022-11-01 hosp_ped 1366 1279 87 0.0657845
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VA hosp_ped 20751 20542 209 0.010122781
## 2 TN hosp_ped 24245 24487 242 0.009931872
## 3 WV hosp_ped 6604 6545 59 0.008974066
## 4 AL hosp_ped 22813 22996 183 0.007989696
## 5 ID hosp_ped 4511 4537 26 0.005747126
## 6 KY hosp_ped 23849 23972 123 0.005144184
## 7 KS hosp_ped 5434 5460 26 0.004773270
## 8 NJ hosp_ped 21670 21742 72 0.003317055
## 9 UT hosp_ped 11652 11685 33 0.002828127
## 10 SD hosp_ped 4673 4660 13 0.002785814
## 11 AZ hosp_ped 30102 30181 79 0.002620971
## 12 NV hosp_ped 6189 6176 13 0.002102709
## 13 MS hosp_ped 13307 13332 25 0.001876947
## 14 CO hosp_ped 24004 24046 42 0.001748179
## 15 NE hosp_ped 8297 8310 13 0.001565605
## 16 VT hosp_ped 710 709 1 0.001409443
## 17 IA hosp_ped 8667 8655 12 0.001385521
## 18 GA hosp_ped 60199 60277 78 0.001294864
## 19 RI hosp_ped 3965 3970 5 0.001260239
## 20 OH hosp_ped 104667 104552 115 0.001099327
##
##
##
## Raw file for cdcHosp:
## Rows: 54,011
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Warning: One or more parsing issues, see `problems()` for details## Rows: 37016 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (102): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
## lgl (5): Administered_Bivalent, Admin_Bivalent_PFR, Admin_Bivalent_MOD, Di...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Bivalent_Booster_5Plus Bivalent_Booster_5Plus_Pop_Pct Bivalent_Booster_12Plus Bivalent_Booster_12Plus_Pop_Pct Bivalent_Booster_18Plus Bivalent_Booster_18Plus_Pop_Pct Bivalent_Booster_65Plus Bivalent_Booster_65Plus_Pop_Pct
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 5
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 37,016
## Columns: 109
## $ date <date> 2022-11-30, 2022-11-30, 2022-1…
## $ MMWR_week <dbl> 48, 48, 48, 48, 48, 48, 48, 48,…
## $ state <chr> "IL", "MN", "IH2", "KS", "PR", …
## $ Distributed <dbl> 35820575, 17080960, 3807595, 77…
## $ Distributed_Janssen <dbl> 1181600, 510200, 111300, 260900…
## $ Distributed_Moderna <dbl> 12292140, 5846320, 1595280, 293…
## $ Distributed_Pfizer <dbl> 22323335, 10712340, 2093815, 45…
## $ Distributed_Novavax <dbl> 23500, 12100, 7200, 18600, 1500…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 282679, 302874, 182810, 266783,…
## $ Distributed_Per_100k_5Plus <dbl> 300385, 323013, 0, 284907, 2738…
## $ Distributed_Per_100k_12Plus <dbl> 330515, 357485, 0, 317014, 2957…
## $ Distributed_Per_100k_18Plus <dbl> 363515, 393890, 0, 351197, 3214…
## $ Distributed_Per_100k_65Plus <dbl> 1753130, 1855860, 0, 1634580, 1…
## $ vxa <dbl> 26067241, 12322471, 2490085, 52…
## $ Administered_5Plus <dbl> 25898457, 12209677, 2478640, 51…
## $ Administered_12Plus <dbl> 24821587, 11683331, 2371842, 49…
## $ Administered_18Plus <dbl> 23174667, 10944712, 2167615, 46…
## $ Administered_65Plus <dbl> 6711958, 3503794, 464502, 15307…
## $ Administered_Janssen <dbl> 688075, 360740, 41955, 138771, …
## $ Administered_Moderna <dbl> 9216654, 4363261, 1137019, 1956…
## $ Administered_Pfizer <dbl> 16125129, 7590451, 1310275, 310…
## $ Administered_Novavax <dbl> 1989, 962, 33, 561, 67, 103, 0,…
## $ Administered_Unk_Manuf <dbl> 35394, 7057, 803, 7846, 413, 26…
## $ Admin_Per_100k <dbl> 205710, 218498, 119554, 178884,…
## $ Admin_Per_100k_5Plus <dbl> 217180, 230894, 0, 189803, 2576…
## $ Admin_Per_100k_12Plus <dbl> 229028, 244519, 0, 203589, 2655…
## $ Admin_Per_100k_18Plus <dbl> 235182, 252387, 0, 210868, 2661…
## $ Admin_Per_100k_65Plus <dbl> 328497, 380689, 0, 321927, 2993…
## $ Recip_Administered <dbl> 26377607, 12338493, 2490085, 53…
## $ Administered_Dose1_Recip <dbl> 9986462, 4410896, 1150249, 2202…
## $ Administered_Dose1_Pop_Pct <dbl> 78.8, 78.2, 55.2, 75.6, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus <dbl> 9895582, 4353096, 1143327, 2185…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 83.0, 82.3, 0.0, 80.1, 95.0, 91…
## $ Administered_Dose1_Recip_12Plus <dbl> 9390809, 4110756, 1087669, 2091…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 86.6, 86.0, 0.0, 85.3, 95.0, 95…
## $ Administered_Dose1_Recip_18Plus <dbl> 8672310, 3810055, 989187, 19402…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 88.0, 87.9, 0.0, 87.7, 95.0, 95…
## $ Administered_Dose1_Recip_65Plus <dbl> 2010346, 970402, 185643, 509306…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 0.0, 95.0, 95.0, 95…
## $ vxc <dbl> 8985591, 4040479, 909384, 18870…
## $ vxcpoppct <dbl> 70.9, 71.6, 43.7, 64.8, 86.7, 7…
## $ Series_Complete_5Plus <dbl> 8939399, 4010594, 907718, 18791…
## $ Series_Complete_5PlusPop_Pct <dbl> 75.0, 75.8, 0.0, 68.9, 89.9, 76…
## $ Series_Complete_12Plus <dbl> 8492371, 3795092, 865244, 18033…
## $ Series_Complete_12PlusPop_Pct <dbl> 78.4, 79.4, 0.0, 73.6, 91.4, 80…
## $ vxcgte18 <dbl> 7836755, 3511601, 785952, 16728…
## $ vxcgte18pct <dbl> 79.5, 81.0, 0.0, 75.6, 91.3, 81…
## $ vxcgte65 <dbl> 1859572, 915212, 147313, 458916…
## $ vxcgte65pct <dbl> 91.0, 95.0, 0.0, 95.0, 92.7, 95…
## $ Series_Complete_Janssen <dbl> 629676, 331415, 39283, 128169, …
## $ Series_Complete_Moderna <dbl> 2909903, 1348661, 407038, 66257…
## $ Series_Complete_Pfizer <dbl> 5419112, 2344114, 462137, 10896…
## $ Series_Complete_Novavax <dbl> 648, 356, 12, 164, 18, 43, 0, 7…
## $ Series_Complete_Unk_Manuf <dbl> 11311, 2433, 36, 1838, 175, 882…
## $ Series_Complete_Janssen_5Plus <dbl> 629624, 331400, 39257, 128166, …
## $ Series_Complete_Moderna_5Plus <dbl> 2887350, 1336756, 406316, 65940…
## $ Series_Complete_Pfizer_5Plus <dbl> 5410509, 2339662, 462097, 10896…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 11268, 2420, 36, 1822, 174, 881…
## $ Series_Complete_Janssen_12Plus <dbl> 629611, 331395, 39256, 128153, …
## $ Series_Complete_Moderna_12Plus <dbl> 2884429, 1334665, 406156, 65907…
## $ Series_Complete_Pfizer_12Plus <dbl> 4966680, 2126329, 419786, 10142…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 11008, 2348, 34, 1723, 134, 867…
## $ Series_Complete_Janssen_18Plus <dbl> 626940, 330818, 39211, 128019, …
## $ Series_Complete_Moderna_18Plus <dbl> 2876120, 1332048, 405787, 65858…
## $ Series_Complete_Pfizer_18Plus <dbl> 4322551, 1846143, 340909, 88454…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 10533, 2251, 34, 1554, 87, 826,…
## $ Series_Complete_Janssen_65Plus <dbl> 84284, 50875, 3508, 18734, 2415…
## $ Series_Complete_Moderna_65Plus <dbl> 827541, 385496, 83343, 213664, …
## $ Series_Complete_Pfizer_65Plus <dbl> 943551, 478218, 60457, 225923, …
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 4091, 571, 3, 563, 19, 397, 0, …
## $ Additional_Doses <dbl> 5058586, 2517728, 330700, 91110…
## $ Additional_Doses_Vax_Pct <dbl> 56.3, 62.3, 36.4, 48.3, 63.4, 4…
## $ Additional_Doses_5Plus <dbl> 5058455, 2517643, 330671, 91103…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 56.6, 62.8, 36.4, 48.5, 63.5, 4…
## $ Additional_Doses_12Plus <dbl> 4954435, 2458206, 322848, 89597…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 58.3, 64.8, 37.3, 49.7, 67.0, 5…
## $ Additional_Doses_18Plus <dbl> 4710452, 2332586, 300090, 85693…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 60.1, 66.4, 38.2, 51.2, 67.2, 5…
## $ Additional_Doses_50Plus <dbl> 2846371, 1429271, 178063, 56429…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 72.9, 78.2, 46.8, 63.7, 73.8, 6…
## $ Additional_Doses_65Plus <dbl> 1524153, 785711, 80383, 332257,…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 82.0, 85.9, 54.6, 72.4, 75.5, 7…
## $ Additional_Doses_Moderna <dbl> 1969929, 961044, 150035, 375247…
## $ Additional_Doses_Pfizer <dbl> 3029303, 1526541, 177899, 52359…
## $ Additional_Doses_Janssen <dbl> 56543, 29430, 2510, 11597, 1229…
## $ Additional_Doses_Unk_Manuf <dbl> 2730, 643, 255, 630, 43, 125, 0…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 1531239, 887062, 69869, 273881,…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 53.8, 62.1, 39.2, 48.5, 33.0, 4…
## $ Second_Booster_65Plus <dbl> 949262, 555729, 36385, 187673, …
## $ Second_Booster_65Plus_Vax_Pct <dbl> 62.3, 70.7, 45.3, 56.5, 38.4, 5…
## $ Second_Booster_Janssen <dbl> 1215, 326, 21, 242, 254, 92, 0,…
## $ Second_Booster_Moderna <dbl> 803070, 418396, 40544, 133625, …
## $ Second_Booster_Pfizer <dbl> 1225292, 762327, 51873, 202315,…
## $ Second_Booster_Unk_Manuf <dbl> 1393, 251, 182, 241, 3, 28, 0, …
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Bivalent_Booster_5Plus <dbl> 1910684, 1111681, 110390, 33765…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 16.0, 21.0, 0.0, 12.4, 5.3, 9.0…
## $ Bivalent_Booster_12Plus <dbl> 1872346, 1087954, 107492, 33233…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 17.3, 22.8, 0.0, 13.6, 5.7, 9.9…
## $ Bivalent_Booster_18Plus <dbl> 1809214, 1047907, 100102, 32131…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 18.4, 24.2, 0.0, 14.5, 6.1, 10.…
## $ Bivalent_Booster_65Plus <dbl> 807922, 482764, 32667, 164039, …
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 39.5, 52.5, 0.0, 34.5, 12.0, 24…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 6.29e+9 8.69e+7 98777220 1076266 8850
## 2 after 6.24e+9 8.65e+7 97618392 1070208 7650
## 3 pctchg 7.69e-3 4.74e-3 0.0117 0.00563 0.136
##
##
## Processed for cdcWeeklyBurden:
## Rows: 53,244
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.36e+7 4.68e+7 1354468 54011
## 2 after 5.33e+7 4.66e+7 1328009 51567
## 3 pctchg 5.44e-3 5.20e-3 0.0195 0.0453
##
##
## Processed for cdcHosp:
## Rows: 51,567
## Columns: 5
## $ date <date> 2021-01-23, 2021-01-23, 2021-01-22, 2021-01-22, 2021-01-21…
## $ state <chr> "ME", "SD", "MD", "RI", "ID", "PA", "ME", "SD", "MA", "AR",…
## $ inp <dbl> 219, 152, 2039, 399, 248, 4926, 240, 189, 2098, 1331, 2597,…
## $ hosp_adult <dbl> 218, 148, 2025, 393, 243, 4860, 238, 189, 2069, 1303, 2524,…
## $ hosp_ped <dbl> 1, 4, 14, 6, 5, 66, 2, 0, 29, 28, 73, 2, 3, 24, 20, 4, 10, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.31e+11 1.76e+11 1565197. 4.45e+10 2.31e+6 1.63e+11 1.84e+6 3.70e+4
## 2 after 2.08e+11 8.51e+10 1309752. 2.15e+10 2.04e+6 7.87e+10 1.56e+6 2.93e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.15e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 29,274
## Columns: 9
## $ date <date> 2022-11-30, 2022-11-30, 2022-11-30, 2022-11-30, 2022-11-3…
## $ state <chr> "IL", "MN", "KS", "DE", "AK", "AL", "SD", "CA", "MT", "IA"…
## $ vxa <dbl> 26067241, 12322471, 5211466, 2022968, 1291050, 6873874, 16…
## $ vxc <dbl> 8985591, 4040479, 1887066, 701269, 474042, 2593348, 581183…
## $ vxcpoppct <dbl> 70.9, 71.6, 64.8, 72.0, 64.8, 52.9, 65.7, 74.2, 58.8, 64.0…
## $ vxcgte65 <dbl> 1859572, 915212, 458916, 189858, 81504, 720647, 157975, 54…
## $ vxcgte65pct <dbl> 91.0, 95.0, 95.0, 95.0, 89.0, 84.8, 95.0, 92.5, 88.9, 95.0…
## $ vxcgte18 <dbl> 7836755, 3511601, 1672861, 629614, 416329, 2402013, 518632…
## $ vxcgte18pct <dbl> 79.5, 81.0, 75.6, 81.7, 75.5, 63.0, 77.7, 83.3, 67.9, 74.4…
##
## Integrated per capita data file:
## Rows: 53,457
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
saveToRDS(cdc_daily_221202, ovrWriteError=FALSE)The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20221203 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20221203.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20221203.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 175777 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 175,777
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 3245
## 2 Critical Access Hospitals 47080
## 3 Long Term 11752
## 4 Short Term 113700
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 17
## 2 GU 74
## 3 MP 26
## 4 PR 1771
## 5 VI 67
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 47835 127675 267 175777
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 24132 139018 12627 175777
## 3 icu_beds_used_7_day_avg 26480 130965 18332 175777
## 4 inpatient_beds_7_day_avg 6616 168497 664 175777
## 5 inpatient_beds_used_7_day_avg 6616 154991 14170 175777
## 6 inpatient_beds_used_covid_7_day_avg 1445 117772 56560 175777
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 24125 103430 48222 175777
## 8 total_adult_patients_hospitalized_confirmed_and… 22166 104235 49376 175777
## 9 total_beds_7_day_avg 28761 146861 155 175777
## 10 total_icu_beds_7_day_avg 2646 164273 8858 175777
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20221203, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_221202 <- postProcessCDCDaily(cdc_daily_221202,
dataThruLabel="Nov 2022",
keyDatesBurden=c("2022-11-30", "2022-05-31",
"2021-11-30", "2021-05-31"
),
keyDatesVaccine=c("2022-11-30", "2021-11-30",
"2021-07-31", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_221202 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_221202,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_221202$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.89e4 54011
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.99e5 54011
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.31e5 54011
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.13e5 54011
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.21e5 54011
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.09e6 54011
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.11e6 54011
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 1.02e6 54011
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.11e4 54011
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.74e5 54011
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.60e5 54011
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.64e5 54011
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.75e5 54011
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.98e5 54011
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.80e5 54011
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 7.12e5 54011
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.83e5 54011
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.22e5 54011saveToRDS(burdenPivotList_221202, ovrWriteError=FALSE)
saveToRDS(hospPerCap_221202, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_221202)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 8,772 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 8,762 more rows
## # ℹ Use `print(n = ...)` to see more rowsThe process should be updated to reflect that there can be multiple peaks around the same time period due to the underlying data being weekly rather than daily. Vaccines data should also be double-checked
Hospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20221202 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20221203")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20221202 <- hospitalCapacityCDCDaily(multiSourceHosp_20221202,
plotSub="Aug 2020 to Nov 2022\nOld data used pre-2022"
)
The original makePeakValley() and findPeaks() functions are copied for reference:
# Annotate peaks and valleys in CDC daily data
# makePeakValley <- function(df,
# numVar,
# windowWidth,
# rollMean=NULL,
# uqBy=c("date"),
# facetVar=c(),
# fnNumVar=function(x) x,
# fnPeak=function(x) x+100,
# fnValley=function(x) x-100,
# fnGroupFacet=FALSE,
# useTitle="",
# yLab=""
# ) {
#
# # FUNCTION ARGUMENTS
# # df: a data frame or tibble
# # numVar: the numeric variable of interest
# # windowWidth: width of the window for calculating peaks and valleys
# # rollMean: the number of days for rolling mean (NULL means no rolling mean)
# # uqBy: variable that the resutling data should be unique by
# # facetVar: variable for faceting (c() means no facets)
# # fnNumVar: what function should be applied to numVar (e.g., function(x) x/1000)
# # fnPeak: function for plotting the peak labels
# # fnValley: function for plotting the valley labels
# # fnGroupFacet: boolean, should the functions be run separatelt for each facet as a grouping variable?
# # useful for labeling if the goal is to use 0.1*max(yVar) rather than a global peak and valley
# # useTitle: title for plots
# # yLab: y-axis label for plots
#
# # Create named vectors for useTitle and yLab if not passed
# if(is.null(names(useTitle)))
# useTitle <- rep(useTitle, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
# if(is.null(names(yLab)))
# yLab <- rep(yLab, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
#
# # Create named lists for fnNumVar, fnPeak, and fnValley
# tempMakeList <- function(f, n, nms) {
# tempList <- vector("list", length=n)
# for(a in 1:n) tempList[[a]] <- f
# names(tempList) <- nms
# tempList
# }
# if(is.null(names(fnNumVar))) fnNumVar <- tempMakeList(fnNumVar, n=length(numVar), nms=numVar)
# if(is.null(names(fnPeak))) fnPeak <- tempMakeList(fnPeak, n=length(numVar), nms=numVar)
# if(is.null(names(fnValley))) fnValley <- tempMakeList(fnValley, n=length(numVar), nms=numVar)
#
# # Create the relevant data frame
# newDF <- df %>%
# group_by_at(all_of(c(uqBy, facetVar))) %>%
# summarize(across(all_of(numVar), .fns=sum, na.rm=TRUE), .groups="drop") %>%
# group_by_at(all_of(facetVar)) %>%
# mutate(if(!is.null(rollMean)) across(all_of(numVar), .fns=zoo::rollmean, k=rollMean, fill=NA),
# across(all_of(numVar), .fns=findPeaks, width=windowWidth, gt=1, .names="{.col}_isPeak"),
# across(all_of(numVar),
# .fns=findPeaks,
# width=windowWidth,
# FUN=min,
# gt=1,
# lt=NULL,
# fillVal=NA
# ,.names="{.col}_isValley"
# )
# ) %>%
# ungroup()
#
# # Group by the facet variable(s) if not NULL and separate function by facet requested
# if(!is.null(facetVar) & isTRUE(fnGroupFacet)) newDF <- newDF %>% group_by_at(all_of(facetVar))
#
# # Create the relevant plots
# for(keyVar in numVar) {
#
# p1 <- newDF %>%
# mutate(posPeak=fnPeak[[keyVar]](fnNumVar[[keyVar]](get(keyVar))),
# posValley=fnValley[[keyVar]](fnNumVar[[keyVar]](get(keyVar)))
# ) %>%
# ggplot(aes(x=get(uqBy), y=fnNumVar[[keyVar]](get(keyVar)))) +
# geom_line() +
# geom_point(data=~filter(., get(paste0(keyVar, "_isPeak"))), color="red", size=3) +
# geom_point(data=~filter(., get(paste0(keyVar, "_isValley"))), color="green", size=3) +
# geom_text(data=~filter(., get(paste0(keyVar, "_isPeak"))),
# aes(y=posPeak,
# label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
# ),
# color="red",
# size=3
# ) +
# geom_text(data=~filter(., get(paste0(keyVar, "_isValley"))),
# aes(y=posValley,
# label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
# ),
# color="black",
# size=3
# ) +
# labs(x=NULL,
# y=yLab[[keyVar]],
# title=useTitle[[keyVar]],
# subtitle="Red (peaks) and green (valleys)"
# )
# if(length(facetVar) > 0) p1 <- p1 + facet_wrap(~get(facetVar), scales="free_y")
#
# print(p1)
#
# }
#
# # Return the data, removing any grouping
# newDF %>% ungroup()
#
# }
# # Function to find local extrema in a vector
# findPeaks <- function(x,
# width=1,
# align="center",
# FUN=max,
# gt=if(identical(FUN, max)) 0 else NULL,
# lt=if(identical(FUN, min)) 0 else NULL,
# fillVal=if(identical(FUN, max)) gt else if(identical(FUN, min)) lt else NA,
# epsTol=1e-12,
# returnBool=TRUE,
# ...
# ) {
#
# # FUNCTION ARGUMENTS:
# # x: a numeric vector
# # width: the width of the window to use
# # align: whether the window should be "center", "left", or "right"
# # FUN: the function to be used (max to find peaks, min to find valleys)
# # gt: to be defined, the value must be greater than gt (NULL means use any value)
# # lt: to be defined, the value must be less than lt (NULL means use any value)
# # fillVal: value to use as output if a window does not exist (too close to boundary)
# # epsTol: the epsilon value for considering two values to be the same
# # returnBool: should the boolean be returned? TRUE means return TRUE/FALSE for peaks, FALSE means return vector
# # ...: any other arguments to be passed to zoo::rollapply()
#
# # Create the rolling data
# rolls <- zoo::rollapply(x, width=width, align=align, FUN=FUN, fill=fillVal, ...)
#
# # No post-processing applied unless returnBool is TRUE
# if(!isTRUE(returnBool)) return(rolls)
#
# # Post-processing managed for gt and lt
# if(!is.null(gt)) rolls <- ifelse(rolls<=gt, NA, rolls)
# if(!is.null(lt)) rolls <- ifelse(rolls>=lt, NA, rolls)
#
# # Return the boolean vector
# !is.na(rolls) & (abs(rolls-x) <= epsTol)
#
# }
# Example for issue with weekly data converted to daily
testVec <- c(rep(c(3,2,4,5,2,3), each=7))
tibble::tibble(x=1:42, y1=testVec, y2=zoo::rollmean(testVec, 7, fill=NA), c=findPeaks(testVec, width=7)) %>%
pivot_longer(-c(x, c)) %>%
ggplot(aes(x=x, y=value, color=c)) +
geom_point() +
labs(title="Example for findPeaks()", x="Index", y="Value") +
facet_wrap(~c("y1"="Original", "y2"="Rolling 7")[name]) +
scale_fill_discrete("Identified as peak?")## Warning: Removed 6 rows containing missing values (`geom_point()`).
# Example for rolling-7 data
testVec2 <- zoo::rollmean(testVec, k=7, fill=0)
tibble::tibble(x=1:42, y1=testVec2, c=findPeaks(testVec2, width=7)) %>%
pivot_longer(-c(x, c)) %>%
ggplot(aes(x=x, y=value, color=c)) +
geom_point() +
labs(title="Example for findPeaks()", x="Index", y="Value") +
scale_fill_discrete("Identified as peak?")
Issues to address include 1) the midpoint always identified as a maximum, and 2) multiple tied values all identified as peaks. The issue appears to be addressed by first passing the data as rolling-7
The definition for maximum can be updated for improved precision. Using the concept of window k (assuming an odd number and “center”, this means looking (k-1)/2 in each direction), then a peak can be defined as:
- The value is greater than all of the previous (k-1)/2 points
- The value is at least as high as all of the next (k-1)/2 points
This should preclude finding points in the middle of an equal range even when they are clearly not peaks. An example includes:
# Example for plain data
tibble::tibble(x=1:42,
y=testVec,
testGT=testVec-zoo::rollapply(lag(testVec, 1), width=3, FUN=max, fill=NA, align="right"),
testGTE=testVec-zoo::rollapply(lead(testVec, 1), width=3, FUN=max, fill=NA, align="left"),
isPeak=(testGT>0) & (testGTE>=0)
) %>%
pivot_longer(-c(x, y)) %>%
mutate(color=ifelse(name=="isPeak",
ifelse(!is.na(value) & value>0, "01. Peak", "02. Not Peak"),
case_when(is.na(value) ~ "06. NA",
value==0 ~ "04. Zero",
value>0 ~ "03. GT",
value<0 ~ "05. LT"
)
)
) %>%
ggplot(aes(x=x, y=y, color=color)) +
geom_point() +
geom_point(data=~filter(., name=="isPeak", color=="01. Peak"), shape=21, size=10) +
facet_wrap(~c("testGT"="Previous 3", "testGTE"="Next 3", "isPeak"="Identified as Peak")[name]) +
scale_color_discrete("Versus closest 3?") +
labs(title="Exploring options for findPeaks()", x="Index", y="Value")
# Example for rolling-7 data
tibble::tibble(x=1:42,
y=testVec2,
testGT=testVec2-zoo::rollapply(lag(testVec2, 1), width=3, FUN=max, fill=NA, align="right"),
testGTE=testVec2-zoo::rollapply(lead(testVec2, 1), width=3, FUN=max, fill=NA, align="left"),
isPeak=(testGT>0) & (testGTE>=0)
) %>%
pivot_longer(-c(x, y)) %>%
mutate(color=ifelse(name=="isPeak",
ifelse(!is.na(value) & value>0, "01. Peak", "02. Not Peak"),
case_when(is.na(value) ~ "06. NA",
value==0 ~ "04. Zero",
value>0 ~ "03. GT",
value<0 ~ "05. LT"
)
)
) %>%
ggplot(aes(x=x, y=y, color=color)) +
geom_point() +
geom_point(data=~filter(., name=="isPeak", color=="01. Peak"), shape=21, size=10) +
facet_wrap(~c("testGT"="Previous 3", "testGTE"="Next 3", "isPeak"="Identified as Peak")[name]) +
scale_color_discrete("Versus closest 3?") +
labs(title="Exploring options for findPeaks()", x="Index", y="Value")
The approaches are compared:
# Example for plain data
tibble::tibble(x=1:42,
y=testVec,
testGT=testVec-zoo::rollapply(lag(testVec, 1), width=3, FUN=max, fill=NA, align="right"),
testGTE=testVec-zoo::rollapply(lead(testVec, 1), width=3, FUN=max, fill=NA, align="left"),
isPeak=(testGT>0) & (testGTE>=0),
findPeak=findPeaks(testVec, width=7)
) %>%
pivot_longer(-c(x, y)) %>%
mutate(color=ifelse(name %in% c("isPeak", "findPeak"),
ifelse(!is.na(value) & value>0, "01. Peak", "02. Not Peak"),
case_when(is.na(value) ~ "06. NA",
value==0 ~ "04. Zero",
value>0 ~ "03. GT",
value<0 ~ "05. LT"
)
)
) %>%
ggplot(aes(x=x, y=y, color=color)) +
geom_point() +
geom_point(data=~filter(., name %in% c("isPeak", "findPeak"), color=="01. Peak"), shape=21, size=10) +
facet_wrap(~c("testGT"="Previous 3",
"testGTE"="Next 3",
"isPeak"="Identified as Peak (new)",
"findPeak"="Identified as Peak (fnPeak)"
)[name]) +
scale_color_discrete("Versus closest 3?") +
labs(title="Exploring options for findPeaks()", x="Index", y="Value")
# Example for rolling-7 data
tibble::tibble(x=1:42,
y=testVec2,
testGT=testVec2-zoo::rollapply(lag(testVec2, 1), width=3, FUN=max, fill=NA, align="right"),
testGTE=testVec2-zoo::rollapply(lead(testVec2, 1), width=3, FUN=max, fill=NA, align="left"),
isPeak=(testGT>0) & (testGTE>=0),
findPeak=findPeaks(testVec2, width=7)
) %>%
pivot_longer(-c(x, y)) %>%
mutate(color=ifelse(name %in% c("isPeak", "findPeak"),
ifelse(!is.na(value) & value>0, "01. Peak", "02. Not Peak"),
case_when(is.na(value) ~ "06. NA",
value==0 ~ "04. Zero",
value>0 ~ "03. GT",
value<0 ~ "05. LT"
)
)
) %>%
ggplot(aes(x=x, y=y, color=color)) +
geom_point() +
geom_point(data=~filter(., name %in% c("isPeak", "findPeak"), color=="01. Peak"), shape=21, size=10) +
facet_wrap(~c("testGT"="Previous 3",
"testGTE"="Next 3",
"isPeak"="Identified as Peak (new)",
"findPeak"="Identified as Peak (fnPeak)"
)[name]) +
scale_color_discrete("Versus closest 3?") +
labs(title="Exploring options for findPeaks()", x="Index", y="Value")
Both methods identify the same peaks when the data are clearly rising and falling. The newer approach is more robust to long periods of flat data, as are more likely to occur when the underlying dataset is weekly converted to daily converted to rolling rather than daily converted directly to rolling
Peak identification is converted to functional form:
# Function to find local extrema in a vector
updatedFindPeaks <- function(x,
width=1,
FUN=max,
# gt=if(identical(FUN, max)) 0 else NULL,
# lt=if(identical(FUN, min)) 0 else NULL,
fillVal=NA,
epsTol=1e-12,
...
) {
# FUNCTION ARGUMENTS:
# x: a numeric vector
# width: the width of the window to use
# FUN: the function to be used (max to find peaks, min to find valleys)
# gt: to be defined, the value must be greater than gt (NULL means use any value)
# lt: to be defined, the value must be less than lt (NULL means use any value)
# fillVal: value to use as output if a window does not exist (too close to boundary)
# epsTol: the epsilon value for considering two values to be the same
# ...: any other arguments to be passed to zoo::rollapply()
# If width is 1 or under, everything is a maximum or minimum
if(width <= 1) return(rep(TRUE, length(x)))
# Check for window width (if even number, look further left than right)
lookLeft <- ceiling((width-1)/2)
lookRight <- floor((width-1)/2)
# Run the look to the left
xLeft <- x-zoo::rollapply(lag(x, 1), width=lookLeft, FUN=FUN, fill=fillVal, align="right")
# Run the look to the right, unless lookRight is 0
if(lookRight==0) xRight <- rep(0, length(x))
else xRight <- x-zoo::rollapply(lead(x, 1), width=lookRight, FUN=FUN, fill=fillVal, align="left")
# Return the boolean vector (only enabled for MAX and MIN for now)
if(identical(FUN, max)) { !is.na(xLeft) & !is.na(xRight) & (xLeft>epsTol) & (xRight>=(-epsTol)) }
else if(identical(FUN, min)) { !is.na(xLeft) & !is.na(xRight) & (xLeft<(-epsTol)) & (xRight<=epsTol) }
else rep(NA, length(x))
}
which(updatedFindPeaks(testVec, width=7))## [1] 15 22 36which(updatedFindPeaks(testVec2, width=7))## [1] 4 25 39which(updatedFindPeaks(testVec, width=2))## [1] 15 22 36which(updatedFindPeaks(testVec2, width=2))## [1] 4 12 13 14 15 16 17 18 19 20 21 22 23 24 25 33 34 35 36 37 38 39which(updatedFindPeaks(testVec, width=7, FUN=min))## [1] 8 29which(updatedFindPeaks(testVec2, width=7, FUN=min))## [1] 11 32The function is identifying peaks as expected. The function is tested against existing coronavirus deaths data:
dfTestBurden <- cdc_daily_221202$dfPerCapita %>%
select(date, state, new_cases, new_deaths) %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
group_by(regn, date) %>%
summarize(across(where(is.numeric), specNA(sum)), .groups="drop")
dfTestBurden %>%
group_by(regn) %>%
mutate(useDeaths=zoo::rollmean(new_deaths, k=7, fill=NA)) %>%
mutate(isPeak=findPeaks(new_deaths, width=71, align="center", FUN=max)) %>%
filter(isPeak)## # A tibble: 27 × 6
## # Groups: regn [4]
## regn date new_cases new_deaths useDeaths isPeak
## <chr> <date> <dbl> <dbl> <dbl> <lgl>
## 1 North Central 2020-04-29 42631 2640 377. TRUE
## 2 North Central 2020-12-16 319169 6501 929. TRUE
## 3 North Central 2021-05-05 86043 1215 174. TRUE
## 4 North Central 2021-09-29 181497 2137 305. TRUE
## 5 North Central 2021-12-08 312920 5417 774. TRUE
## 6 North Central 2022-01-26 835474 4546 649. TRUE
## 7 North Central 2022-06-29 118863 615 87.9 TRUE
## 8 North Central 2022-09-21 91189 700 100 TRUE
## 9 Northeast 2020-04-15 119753 10447 1492. TRUE
## 10 Northeast 2021-01-13 299516 4461 637. TRUE
## # … with 17 more rowsdfTestBurden %>%
group_by(regn) %>%
mutate(useDeaths=zoo::rollmean(new_deaths, k=7, fill=NA)) %>%
mutate(isPeak=updatedFindPeaks(new_deaths, width=71, align="center", FUN=max)) %>%
filter(isPeak)## # A tibble: 27 × 6
## # Groups: regn [4]
## regn date new_cases new_deaths useDeaths isPeak
## <chr> <date> <dbl> <dbl> <dbl> <lgl>
## 1 North Central 2020-04-29 42631 2640 377. TRUE
## 2 North Central 2020-12-16 319169 6501 929. TRUE
## 3 North Central 2021-05-05 86043 1215 174. TRUE
## 4 North Central 2021-09-29 181497 2137 305. TRUE
## 5 North Central 2021-12-08 312920 5417 774. TRUE
## 6 North Central 2022-01-26 835474 4546 649. TRUE
## 7 North Central 2022-06-29 118863 615 87.9 TRUE
## 8 North Central 2022-09-21 91189 700 100 TRUE
## 9 Northeast 2020-04-15 119753 10447 1492. TRUE
## 10 Northeast 2021-01-13 299516 4461 637. TRUE
## # … with 17 more rowsThe methodologies find the same peaks, so overplotting is not caused by findPeaks. The function should still be updated for better behavior with ties. The overplotting issue needs further exploration as well
An example for deaths is included:
dfPeakDeath <- cdc_daily_221202$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar="new_deaths",
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x),
fnPeak=list("new_deaths"=function(x) x+100),
fnValley=list("new_deaths"=function(x) x-100),
useTitle=c("new_deaths"="US coronavirus deaths"),
yLab=c("new_deaths"=paste0("Rolling ", 7, "-day mean deaths"))
)## Warning: Removed 6 rows containing missing values (`geom_line()`).
dfPeakDeath %>%
filter(new_deaths_isPeak) %>%
group_by(regn) %>%
mutate(idx=1:n()) %>%
ggplot() +
geom_text(aes(x=date, y=idx, label=date)) +
facet_wrap(~regn, scales="free_y") +
labs(x=NULL, y="Index", "Peaks identified in deaths data")
The updated peaks function should resolve the identification of 7 consecutive peaks. Example code:
updatedMakePeakValley <- function(df,
numVar,
windowWidth,
rollMean=NULL,
uqBy=c("date"),
facetVar=c(),
fnNumVar=function(x) x,
fnPeak=function(x) x+100,
fnValley=function(x) x-100,
fnGroupFacet=FALSE,
useTitle="",
yLab=""
) {
# FUNCTION ARGUMENTS
# df: a data frame or tibble
# numVar: the numeric variable of interest
# windowWidth: width of the window for calculating peaks and valleys
# rollMean: the number of days for rolling mean (NULL means no rolling mean)
# uqBy: variable that the resutling data should be unique by
# facetVar: variable for faceting (c() means no facets)
# fnNumVar: what function should be applied to numVar (e.g., function(x) x/1000)
# fnPeak: function for plotting the peak labels
# fnValley: function for plotting the valley labels
# fnGroupFacet: boolean, should the functions be run separatelt for each facet as a grouping variable?
# useful for labeling if the goal is to use 0.1*max(yVar) rather than a global peak and valley
# useTitle: title for plots
# yLab: y-axis label for plots
# Create named vectors for useTitle and yLab if not passed
if(is.null(names(useTitle)))
useTitle <- rep(useTitle, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
if(is.null(names(yLab)))
yLab <- rep(yLab, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
# Create named lists for fnNumVar, fnPeak, and fnValley
tempMakeList <- function(f, n, nms) {
tempList <- vector("list", length=n)
for(a in 1:n) tempList[[a]] <- f
names(tempList) <- nms
tempList
}
if(is.null(names(fnNumVar))) fnNumVar <- tempMakeList(fnNumVar, n=length(numVar), nms=numVar)
if(is.null(names(fnPeak))) fnPeak <- tempMakeList(fnPeak, n=length(numVar), nms=numVar)
if(is.null(names(fnValley))) fnValley <- tempMakeList(fnValley, n=length(numVar), nms=numVar)
# Create the relevant data frame
newDF <- df %>%
group_by_at(all_of(c(uqBy, facetVar))) %>%
summarize(across(all_of(numVar), .fns=sum, na.rm=TRUE), .groups="drop") %>%
group_by_at(all_of(facetVar)) %>%
mutate(if(!is.null(rollMean)) across(all_of(numVar), .fns=zoo::rollmean, k=rollMean, fill=NA),
across(all_of(numVar), .fns=updatedFindPeaks, width=windowWidth, FUN=max, .names="{.col}_isPeak"),
across(all_of(numVar), .fns=updatedFindPeaks, width=windowWidth, FUN=min, .names="{.col}_isValley")
) %>%
ungroup()
# Group by the facet variable(s) if not NULL and separate function by facet requested
if(!is.null(facetVar) & isTRUE(fnGroupFacet)) newDF <- newDF %>% group_by_at(all_of(facetVar))
# Create the relevant plots
for(keyVar in numVar) {
p1 <- newDF %>%
mutate(posPeak=fnPeak[[keyVar]](fnNumVar[[keyVar]](get(keyVar))),
posValley=fnValley[[keyVar]](fnNumVar[[keyVar]](get(keyVar)))
) %>%
ggplot(aes(x=get(uqBy), y=fnNumVar[[keyVar]](get(keyVar)))) +
geom_line() +
geom_point(data=~filter(., get(paste0(keyVar, "_isPeak"))), color="red", size=3) +
geom_point(data=~filter(., get(paste0(keyVar, "_isValley"))), color="green", size=3) +
geom_text(data=~filter(., get(paste0(keyVar, "_isPeak"))),
aes(y=posPeak,
label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
),
color="red",
size=3
) +
geom_text(data=~filter(., get(paste0(keyVar, "_isValley"))),
aes(y=posValley,
label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
),
color="black",
size=3
) +
labs(x=NULL,
y=yLab[[keyVar]],
title=useTitle[[keyVar]],
subtitle="Red (peaks) and green (valleys)"
)
if(length(facetVar) > 0) p1 <- p1 + facet_wrap(~get(facetVar), scales="free_y")
print(p1)
}
# Return the data, removing any grouping
newDF %>% ungroup()
}
dfPeakDeath_v2 <- cdc_daily_221202$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
updatedMakePeakValley(numVar="new_deaths",
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x),
fnPeak=list("new_deaths"=function(x) x+100),
fnValley=list("new_deaths"=function(x) x-100),
useTitle=c("new_deaths"="US coronavirus deaths"),
yLab=c("new_deaths"=paste0("Rolling ", 7, "-day mean deaths"))
)## Warning: Removed 6 rows containing missing values (`geom_line()`).
dfPeakDeath_v2 %>%
filter(new_deaths_isPeak) %>%
group_by(regn) %>%
mutate(idx=1:n()) %>%
ggplot() +
geom_text(aes(x=date, y=idx, label=date)) +
facet_wrap(~regn, scales="free_y") +
labs(x=NULL, y="Index", "Peaks identified in deaths data")
The overplotting issue is addressed. There are still issues of how much lift should be required to declare a peak, especially as a time series becomes long. The function is run for all the vaccine variables:
dfPeaksVax <- cdc_daily_221202$dfPerCapita %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(c("state", "date", "vxc", "vxa")) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
filter(date >= as.Date("2020-12-15")) %>%
makePeakValley(numVar=c("vxa", "vxc"),
windowWidth = 71,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"=paste0("Vaccines adminsitered (states with population >= ", 8000000, ")"),
"vxc"=paste0("Became fully vaccinated (states with population >= ", 8000000, ")")
),
yLab=c("vxa"=paste0("Rolling ", 21, "-day mean administered (000)"),
"vxc"=paste0("Rolling ", 21,"-day mean completed (000)")
)
)## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
dfPeaksVax %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
select(date, regn, contains("isPeak")) %>%
pivot_longer(-c(date, regn)) %>%
filter(value) %>%
group_by(regn, name) %>%
mutate(idx=1:n()) %>%
ggplot() +
geom_text(aes(x=date, y=factor(idx), label=date), size=3) +
facet_grid(name~regn) +
labs(x=NULL, y="Index", "Peaks identified in vaccines data")
The function is run for all the burden variables:
dfPeaks <- cdc_daily_221202$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
updatedMakePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"=paste0("Rolling ", 7, "-day mean deaths"),
"new_cases"=paste0("Rolling ", 7, "-day mean cases (000)"),
"inp"=paste0("Rolling ", 7, "-day mean in hospital (000)")
)
)## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
dfPeaks %>%
select(date, regn, contains("isPeak")) %>%
pivot_longer(-c(date, regn)) %>%
filter(value) %>%
group_by(regn, name) %>%
mutate(idx=1:n()) %>%
ggplot() +
geom_text(aes(x=date, y=factor(idx), label=date), size=3) +
facet_grid(name~regn) +
labs(x=NULL, y="Index", "Peaks identified in burden data")
The vaccines data also need to be converted for weekly rather than daily input beginning in June 2022. The convertWeeklyDaily() function is tested:
convertWeeklyDaily <- function(df,
timeVar="date",
otherVars=c("state"),
timeUnitDays=1,
locfVars=c("tot_cases", "tot_deaths"),
naVars=c("new_cases", "new_deaths")
) {
# FUNCTION ARGUMENTS:
# df: the original daily frame
# timeVar: the time variable in the original dataset
# otherVars: other variables of importance (df should be unique by otherVars-timeVar)
# timeUnitDays: final data should be every 'timeUnitDays' days (e.g., 2 would be every other day)
# locfVars: variables for applying zoo::na.locf
# naVars: variables for applying ifelse(is.na(x), 0, x)
# Check that all variables are included
nms1 <- sort(names(df))
nms2 <- sort(c(timeVar, otherVars, locfVars, naVars))
if(!isTRUE(all.equal(nms1, nms2))) {
warning(paste0("\nSome variables not passed to convertWeeklyDaily() will be treated as-is:",
"\ndf has variables: ",
paste0(nms1, collapse=", "),
"\nvariables passed as arguments are: ",
paste0(nms2, collapse=", "),
"\n"
)
)
}
# Relevant times to include
keyTimes <- seq.Date(df %>% pull(timeVar) %>% min, df %>% pull(timeVar) %>% max, by=timeUnitDays) %>%
tibble::tibble() %>%
purrr::set_names(timeVar)
# Relevant levels to include
keyLevels <- df %>% select(all_of(otherVars)) %>% unique()
# Return cross-join tibble
tibble::tibble(keyTimes) %>%
full_join(keyLevels, by=character()) %>%
purrr::set_names(c(timeVar, otherVars)) %>%
left_join(df, by=c(timeVar, otherVars)) %>%
arrange(across(c(all_of(otherVars), all_of(timeVar)))) %>%
group_by(across(all_of(otherVars))) %>%
mutate(across(all_of(locfVars), .fns=function(x) zoo::na.locf(x, na.rm=FALSE)),
across(all_of(naVars), .fns=function(x) ifelse(is.na(x), 0, x))
) %>%
ungroup()
}
keyVars <- cdc_daily_221202$dfPerCapita %>%
select(starts_with("vx")) %>%
names
dfVaxDaily <- cdc_daily_221202$dfPerCapita %>%
select(date, state, all_of(keyVars)) %>%
convertWeeklyDaily(locfVars=keyVars, naVars=c())
dfPeaksVax_v2 <- dfVaxDaily %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(c("state", "date", "vxc", "vxa")) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
filter(date >= as.Date("2020-12-15")) %>%
updatedMakePeakValley(numVar=c("vxa", "vxc"),
windowWidth = 71,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"=paste0("Vaccines adminsitered (states with population >= ", 8000000, ")"),
"vxc"=paste0("Became fully vaccinated (states with population >= ", 8000000, ")")
),
yLab=c("vxa"=paste0("Rolling ", 21, "-day mean administered (000)"),
"vxc"=paste0("Rolling ", 21,"-day mean completed (000)")
)
)## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
dfPeaksVax_v2 %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
select(date, state, contains("isPeak")) %>%
pivot_longer(-c(date, state)) %>%
filter(value) %>%
group_by(state, name) %>%
mutate(idx=1:n()) %>%
ggplot() +
geom_text(aes(x=date, y=factor(idx), label=date), size=3) +
facet_grid(name~state) +
labs(x=NULL, y="Index", "Peaks identified in vaccines data")
The convertWeeklyDaily() function is updated for better flexibility:
makeDaily <- function(df, type="cdcWeeklyBurden") {
# FUNCTION ARGUMENTS:
# df: the data frame
# type: the type of conversion to be run ("cdcWeeklyBurden" or "vax" currently enabled)
# Can run as-is for weekly burden data, convert all numeric variables using zoo::na.locf for vaccines
if(type=="cdcWeeklyBurden") convertWeeklyDaily(df)
else if(type=="vax") convertWeeklyDaily(df, locfVars=df %>% select(where(is.numeric)) %>% names, naVars=c())
else {
warning("\nFunction makeDaily is not available for specified type, returning dataset as-is\n")
df
}
}
# Confirm function works as intended for vaccines
all.equal(makeDaily(cdc_daily_221202$dfPerCapita %>% colSelector(vecSelectMapper[["vax"]]), type="vax"),
dfVaxDaily %>% colSelector(vecSelectMapper[["vax"]])
)## [1] TRUENext steps are to update readRunCDCDaily() for the vaccines conversion process:
# Function to download/load, process, segment, and analyze data for CDC daily (last updated 02-AUG-2021)
readRunCDCDaily <- function(thruLabel,
downloadTo=list("cdcDaily"=NA, "cdcHosp"=NA, "vax"=NA),
readFrom=downloadTo,
compareFile=list("cdcDaily"=NA, "cdcHosp"=NA, "vax"=NA),
writeLog=NULL,
ovrwriteLog=TRUE,
dfPerCapita=NULL,
useClusters=NULL,
hierarchical=TRUE,
returnList=!isTRUE(hierarchical),
kCut=6,
reAssignState=vector("list", 0),
weightedMeanAggs=eval(formals(diagnoseClusters)$wm_aggVars),
detailedPlotAggs=weightedMeanAggs,
skipAssessmentPlots=FALSE,
brewPalette=NA,
convertWeekly=c("cdcWeeklyBurden", "vax"),
...
) {
# FUNCTION ARGUMENTS:
# thruLabel: the label for when the data are through (e.g., "Aug 30, 2020")
# donwloadTo: named list for locations to download data (cdcDaily and cdcHosp)
# NA means do not download data for that particular element
# readFrom: named list for locations to read data from (defaults to donwloadTo)
# compareFile: named list for the reference file to be used for cdcDaily and cdcHosp
# NA means do not use a reference file for that element
# dateChangePlot: boolean, should changes in dates be captured as a plot rather than as a list?
# dateMetricPrint: boolean, should the changes by date and metric be printed to the main log?
# writeLog: name of a separate log file for capturing detailed data on changes between files
# NULL means no detailed data captured
# ovrwriteLog: boolean, should the log file be overwritten and started again from scratch?
# dfPerCapita: file can be passed directly, which bypasses the loading and processing steps
# default NULL means create dfPerCapita using steps 2-4
# useClusters: file containing clusters by state (NULL means make the clusters from the data)
# hierarchical: boolean, should hierarchical clusters be produced (if FALSE, will be k-means)?
# returnList: boolean, should a list be returned or just the cluster object?
# refers to what is returned by clusterStates(); the main function always returns a list
# kCut: number of segments when cutting the hierarchical tree
# reAssignState: mapping file for assigning a state to another state's cluster
# format list("stateToChange"="stateClusterToAssign")
# weightedMeanAggs: variables where a population-weighted cluster mean should be created
# detailedPlotAggs: variables that should be included in the cluster-level disease evolution detailed plots
# skipAssessmentPlots: boolean to skip the plots for assessClusters()
# especially useful if just exploring dendrograms or silhouette widths
# brewPalette: create plots using this color scheme (needs to be valid in ggplot2::scale_*_brewer())
# NA means use R default color schemes
# convertWeekly: signals that dfProcess for this data frame should be passed through convertWeeklyDaily()
# ...: arguments to be passed to clusterStates(), will be used only if useClusters is NULL
# STEP 0: Function to create the return list
createFinalList <- function(plots=TRUE) {
list(stateData=stateData,
dfRaw=dfRawList,
dfProcess=dfProcessList,
dfPerCapita=dfPerCapita,
useClusters=useClusters,
plotDataList=if(plots) plotDataList else NULL
)
}
# STEP 1: Get state data
stateData <- getStateData()
# If a log file is requested, create the log file (allows for append=TRUE for all downstream functions)
if (!is.null(writeLog)) genNewLog(writeLog=writeLog, ovrwriteLog=ovrwriteLog)
# Get the data types to be used (will be the elements of readFrom) and create a file storage list
elemUsed <- names(readFrom)
dfRawList <- vector("list", length=length(elemUsed)) %>% purrr::set_names(elemUsed)
dfProcessList <- vector("list", length=length(elemUsed)) %>% purrr::set_names(elemUsed)
# Steps 2-4 are run only is dfPerCapita has not been passed
if (is.null(dfPerCapita)) {
# Step 2: Download and QC all of the requested data
for (elem in elemUsed) {
dfRawList[[elem]] <- readQCRawCDCDaily(fileName=readFrom[[elem]],
writeLog=writeLog,
ovrwriteLog=FALSE,
urlType=elem,
getData=if(is.na(downloadTo[[elem]])) FALSE else TRUE,
dfRef=compareFile[[elem]]
)
glimpseLog(dfRawList[[elem]], txt=paste0("\nRaw file for ", elem, ":\n"), logFile=writeLog)
}
# Step 3: Process all of the requested data
for (elem in elemUsed) {
dfProcessList[[elem]] <- processRawFile(dfRawList[[elem]],
vecRename=c(), # already handled in readQCRawCDCDaily()
vecSelect=vecSelectMapper[[elem]],
lstCombo=lstComboMapper[[elem]],
lstFilter=lstFilterMapper[[elem]]
)
# Run conversions of weekly to daily if needed
if(elem %in% all_of(convertWeekly)) dfProcessList[[elem]] <- makeDaily(dfProcessList[[elem]], type=elem)
glimpseLog(dfProcessList[[elem]], txt=paste0("\nProcessed for ", elem, ":\n"), logFile=writeLog)
}
# Step 4: Integrate in to a dfPerCapita file and glimpse (to specified log file)
dfPerCapita <- createPerCapita(dfProcessList,
uqBy=c("state", "date"),
popData=stateData,
mapper=perCapMapper,
asIsVars=if(isTRUE(exists("asIsMapper"))) unname(unlist(asIsMapper)) else c()
)
glimpseLog(dfPerCapita, txt="\nIntegrated per capita data file:\n", logFile=writeLog)
} else {
dfRawList <- NULL
dfProcessList <- NULL
}
# STEP 5: Create the clusters (if they have not been passed)
if (is.null(useClusters)) {
clData <- clusterStates(df=dfPerCapita, hierarchical=hierarchical, returnList=returnList, ...)
useClusters <- getClusters(clData, hier=hierarchical, kCut=kCut, reAssign=reAssignState)
}
# STEP 5a: Stop the process and return what is available if skipAssessmentPlots is TRUE
if (skipAssessmentPlots)
return(createFinalList(plots=FALSE))
# STEP 6: Create the cluster assessments
lstFuns <- list("stateData"=function(x) colSelector(x, vecSelect=c("state", "pop")),
"dfPerCapita"=NULL
)
plotDataList <- diagnoseClusters(lst=list("stateData"=stateData,
"dfPerCapita"=dfPerCapita,
"useClusters"=useClusters
),
lstExtract=lstFuns,
wm_aggVars=weightedMeanAggs,
detailAggVars=detailedPlotAggs,
brewPalette=brewPalette
)
# STEP 7: Return a list of the key data
return(createFinalList(plots=TRUE))
}The function can then be tested on previously downloaded data:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_221202.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221202.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221202.csv"
)
compareList <- list("cdcWeeklyBurden"=dfTest_v3_ref,
"cdcHosp"=readFromRDS("cdc_daily_221102")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_221102")$dfRaw$vax
)
cdc_daily_221202_v2 <- readRunCDCDaily(thruLabel="Nov 30, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_221202.csv## Rows: 9000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 2
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-05-18 new_deaths 2268 4129 1861 0.5818352
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 IL tot_deaths 3393067 3354725 38342 0.011364310
## 2 FSM tot_deaths 599 605 6 0.009966777
## 3 NY tot_deaths 2690681 2708899 18218 0.006747932
## 4 MP tot_deaths 1776 1772 4 0.002254791
## 5 DE tot_deaths 257554 257087 467 0.001814857
## 6 DE tot_cases 19368447 19422200 53753 0.002771441
## 7 NY tot_cases 204316373 204543867 227494 0.001112820
## 8 FSM new_deaths 55 58 3 0.053097345
## 9 NY new_deaths 30317 30541 224 0.007361399
## 10 CO new_deaths 13555 13506 49 0.003621448
## 11 DE new_deaths 3172 3163 9 0.002841358
## 12 NY new_cases 3331848 3340730 8882 0.002662239
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 9,000
## Columns: 10
## $ date_updated <date> 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-13, 2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <date> 2020-01-16, 2020-01-23, 2020-01-30, 2020-02-06, 2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_221202.csv## Rows: 54011 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 30
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 5318 6791 1473 0.2432901
## 2 2020-07-25 hosp_ped 5434 4508 926 0.1862804
## 3 2022-11-01 hosp_ped 1366 1279 87 0.0657845
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VA hosp_ped 20751 20542 209 0.010122781
## 2 TN hosp_ped 24245 24487 242 0.009931872
## 3 WV hosp_ped 6604 6545 59 0.008974066
## 4 AL hosp_ped 22813 22996 183 0.007989696
## 5 ID hosp_ped 4511 4537 26 0.005747126
## 6 KY hosp_ped 23849 23972 123 0.005144184
## 7 KS hosp_ped 5434 5460 26 0.004773270
## 8 NJ hosp_ped 21670 21742 72 0.003317055
## 9 UT hosp_ped 11652 11685 33 0.002828127
## 10 SD hosp_ped 4673 4660 13 0.002785814
## 11 AZ hosp_ped 30102 30181 79 0.002620971
## 12 NV hosp_ped 6189 6176 13 0.002102709
## 13 MS hosp_ped 13307 13332 25 0.001876947
## 14 CO hosp_ped 24004 24046 42 0.001748179
## 15 NE hosp_ped 8297 8310 13 0.001565605
## 16 VT hosp_ped 710 709 1 0.001409443
## 17 IA hosp_ped 8667 8655 12 0.001385521
## 18 GA hosp_ped 60199 60277 78 0.001294864
## 19 RI hosp_ped 3965 3970 5 0.001260239
## 20 OH hosp_ped 104667 104552 115 0.001099327
##
##
##
## Raw file for cdcHosp:
## Rows: 54,011
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_221202.csv## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 37016 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (102): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
## lgl (5): Administered_Bivalent, Admin_Bivalent_PFR, Admin_Bivalent_MOD, Di...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Bivalent_Booster_5Plus Bivalent_Booster_5Plus_Pop_Pct Bivalent_Booster_12Plus Bivalent_Booster_12Plus_Pop_Pct Bivalent_Booster_18Plus Bivalent_Booster_18Plus_Pop_Pct Bivalent_Booster_65Plus Bivalent_Booster_65Plus_Pop_Pct
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 5
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 37,016
## Columns: 109
## $ date <date> 2022-11-30, 2022-11-30, 2022-1…
## $ MMWR_week <dbl> 48, 48, 48, 48, 48, 48, 48, 48,…
## $ state <chr> "IL", "MN", "IH2", "KS", "PR", …
## $ Distributed <dbl> 35820575, 17080960, 3807595, 77…
## $ Distributed_Janssen <dbl> 1181600, 510200, 111300, 260900…
## $ Distributed_Moderna <dbl> 12292140, 5846320, 1595280, 293…
## $ Distributed_Pfizer <dbl> 22323335, 10712340, 2093815, 45…
## $ Distributed_Novavax <dbl> 23500, 12100, 7200, 18600, 1500…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 282679, 302874, 182810, 266783,…
## $ Distributed_Per_100k_5Plus <dbl> 300385, 323013, 0, 284907, 2738…
## $ Distributed_Per_100k_12Plus <dbl> 330515, 357485, 0, 317014, 2957…
## $ Distributed_Per_100k_18Plus <dbl> 363515, 393890, 0, 351197, 3214…
## $ Distributed_Per_100k_65Plus <dbl> 1753130, 1855860, 0, 1634580, 1…
## $ vxa <dbl> 26067241, 12322471, 2490085, 52…
## $ Administered_5Plus <dbl> 25898457, 12209677, 2478640, 51…
## $ Administered_12Plus <dbl> 24821587, 11683331, 2371842, 49…
## $ Administered_18Plus <dbl> 23174667, 10944712, 2167615, 46…
## $ Administered_65Plus <dbl> 6711958, 3503794, 464502, 15307…
## $ Administered_Janssen <dbl> 688075, 360740, 41955, 138771, …
## $ Administered_Moderna <dbl> 9216654, 4363261, 1137019, 1956…
## $ Administered_Pfizer <dbl> 16125129, 7590451, 1310275, 310…
## $ Administered_Novavax <dbl> 1989, 962, 33, 561, 67, 103, 0,…
## $ Administered_Unk_Manuf <dbl> 35394, 7057, 803, 7846, 413, 26…
## $ Admin_Per_100k <dbl> 205710, 218498, 119554, 178884,…
## $ Admin_Per_100k_5Plus <dbl> 217180, 230894, 0, 189803, 2576…
## $ Admin_Per_100k_12Plus <dbl> 229028, 244519, 0, 203589, 2655…
## $ Admin_Per_100k_18Plus <dbl> 235182, 252387, 0, 210868, 2661…
## $ Admin_Per_100k_65Plus <dbl> 328497, 380689, 0, 321927, 2993…
## $ Recip_Administered <dbl> 26377607, 12338493, 2490085, 53…
## $ Administered_Dose1_Recip <dbl> 9986462, 4410896, 1150249, 2202…
## $ Administered_Dose1_Pop_Pct <dbl> 78.8, 78.2, 55.2, 75.6, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus <dbl> 9895582, 4353096, 1143327, 2185…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 83.0, 82.3, 0.0, 80.1, 95.0, 91…
## $ Administered_Dose1_Recip_12Plus <dbl> 9390809, 4110756, 1087669, 2091…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 86.6, 86.0, 0.0, 85.3, 95.0, 95…
## $ Administered_Dose1_Recip_18Plus <dbl> 8672310, 3810055, 989187, 19402…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 88.0, 87.9, 0.0, 87.7, 95.0, 95…
## $ Administered_Dose1_Recip_65Plus <dbl> 2010346, 970402, 185643, 509306…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 0.0, 95.0, 95.0, 95…
## $ vxc <dbl> 8985591, 4040479, 909384, 18870…
## $ vxcpoppct <dbl> 70.9, 71.6, 43.7, 64.8, 86.7, 7…
## $ Series_Complete_5Plus <dbl> 8939399, 4010594, 907718, 18791…
## $ Series_Complete_5PlusPop_Pct <dbl> 75.0, 75.8, 0.0, 68.9, 89.9, 76…
## $ Series_Complete_12Plus <dbl> 8492371, 3795092, 865244, 18033…
## $ Series_Complete_12PlusPop_Pct <dbl> 78.4, 79.4, 0.0, 73.6, 91.4, 80…
## $ vxcgte18 <dbl> 7836755, 3511601, 785952, 16728…
## $ vxcgte18pct <dbl> 79.5, 81.0, 0.0, 75.6, 91.3, 81…
## $ vxcgte65 <dbl> 1859572, 915212, 147313, 458916…
## $ vxcgte65pct <dbl> 91.0, 95.0, 0.0, 95.0, 92.7, 95…
## $ Series_Complete_Janssen <dbl> 629676, 331415, 39283, 128169, …
## $ Series_Complete_Moderna <dbl> 2909903, 1348661, 407038, 66257…
## $ Series_Complete_Pfizer <dbl> 5419112, 2344114, 462137, 10896…
## $ Series_Complete_Novavax <dbl> 648, 356, 12, 164, 18, 43, 0, 7…
## $ Series_Complete_Unk_Manuf <dbl> 11311, 2433, 36, 1838, 175, 882…
## $ Series_Complete_Janssen_5Plus <dbl> 629624, 331400, 39257, 128166, …
## $ Series_Complete_Moderna_5Plus <dbl> 2887350, 1336756, 406316, 65940…
## $ Series_Complete_Pfizer_5Plus <dbl> 5410509, 2339662, 462097, 10896…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 11268, 2420, 36, 1822, 174, 881…
## $ Series_Complete_Janssen_12Plus <dbl> 629611, 331395, 39256, 128153, …
## $ Series_Complete_Moderna_12Plus <dbl> 2884429, 1334665, 406156, 65907…
## $ Series_Complete_Pfizer_12Plus <dbl> 4966680, 2126329, 419786, 10142…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 11008, 2348, 34, 1723, 134, 867…
## $ Series_Complete_Janssen_18Plus <dbl> 626940, 330818, 39211, 128019, …
## $ Series_Complete_Moderna_18Plus <dbl> 2876120, 1332048, 405787, 65858…
## $ Series_Complete_Pfizer_18Plus <dbl> 4322551, 1846143, 340909, 88454…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 10533, 2251, 34, 1554, 87, 826,…
## $ Series_Complete_Janssen_65Plus <dbl> 84284, 50875, 3508, 18734, 2415…
## $ Series_Complete_Moderna_65Plus <dbl> 827541, 385496, 83343, 213664, …
## $ Series_Complete_Pfizer_65Plus <dbl> 943551, 478218, 60457, 225923, …
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 4091, 571, 3, 563, 19, 397, 0, …
## $ Additional_Doses <dbl> 5058586, 2517728, 330700, 91110…
## $ Additional_Doses_Vax_Pct <dbl> 56.3, 62.3, 36.4, 48.3, 63.4, 4…
## $ Additional_Doses_5Plus <dbl> 5058455, 2517643, 330671, 91103…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 56.6, 62.8, 36.4, 48.5, 63.5, 4…
## $ Additional_Doses_12Plus <dbl> 4954435, 2458206, 322848, 89597…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 58.3, 64.8, 37.3, 49.7, 67.0, 5…
## $ Additional_Doses_18Plus <dbl> 4710452, 2332586, 300090, 85693…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 60.1, 66.4, 38.2, 51.2, 67.2, 5…
## $ Additional_Doses_50Plus <dbl> 2846371, 1429271, 178063, 56429…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 72.9, 78.2, 46.8, 63.7, 73.8, 6…
## $ Additional_Doses_65Plus <dbl> 1524153, 785711, 80383, 332257,…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 82.0, 85.9, 54.6, 72.4, 75.5, 7…
## $ Additional_Doses_Moderna <dbl> 1969929, 961044, 150035, 375247…
## $ Additional_Doses_Pfizer <dbl> 3029303, 1526541, 177899, 52359…
## $ Additional_Doses_Janssen <dbl> 56543, 29430, 2510, 11597, 1229…
## $ Additional_Doses_Unk_Manuf <dbl> 2730, 643, 255, 630, 43, 125, 0…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 1531239, 887062, 69869, 273881,…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 53.8, 62.1, 39.2, 48.5, 33.0, 4…
## $ Second_Booster_65Plus <dbl> 949262, 555729, 36385, 187673, …
## $ Second_Booster_65Plus_Vax_Pct <dbl> 62.3, 70.7, 45.3, 56.5, 38.4, 5…
## $ Second_Booster_Janssen <dbl> 1215, 326, 21, 242, 254, 92, 0,…
## $ Second_Booster_Moderna <dbl> 803070, 418396, 40544, 133625, …
## $ Second_Booster_Pfizer <dbl> 1225292, 762327, 51873, 202315,…
## $ Second_Booster_Unk_Manuf <dbl> 1393, 251, 182, 241, 3, 28, 0, …
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Bivalent_Booster_5Plus <dbl> 1910684, 1111681, 110390, 33765…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 16.0, 21.0, 0.0, 12.4, 5.3, 9.0…
## $ Bivalent_Booster_12Plus <dbl> 1872346, 1087954, 107492, 33233…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 17.3, 22.8, 0.0, 13.6, 5.7, 9.9…
## $ Bivalent_Booster_18Plus <dbl> 1809214, 1047907, 100102, 32131…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 18.4, 24.2, 0.0, 14.5, 6.1, 10.…
## $ Bivalent_Booster_65Plus <dbl> 807922, 482764, 32667, 164039, …
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 39.5, 52.5, 0.0, 34.5, 12.0, 24…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 6.29e+9 8.69e+7 98777220 1076266 8850
## 2 after 6.24e+9 8.65e+7 97618392 1070208 7650
## 3 pctchg 7.69e-3 4.74e-3 0.0117 0.00563 0.136
##
##
## Processed for cdcWeeklyBurden:
## Rows: 53,244
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.36e+7 4.68e+7 1354468 54011
## 2 after 5.33e+7 4.66e+7 1328009 51567
## 3 pctchg 5.44e-3 5.20e-3 0.0195 0.0453
##
##
## Processed for cdcHosp:
## Rows: 51,567
## Columns: 5
## $ date <date> 2021-01-23, 2021-01-23, 2021-01-22, 2021-01-22, 2021-01-21…
## $ state <chr> "ME", "SD", "MD", "RI", "ID", "PA", "ME", "SD", "MA", "AR",…
## $ inp <dbl> 219, 152, 2039, 399, 248, 4926, 240, 189, 2098, 1331, 2597,…
## $ hosp_adult <dbl> 218, 148, 2025, 393, 243, 4860, 238, 189, 2069, 1303, 2524,…
## $ hosp_ped <dbl> 1, 4, 14, 6, 5, 66, 2, 0, 29, 28, 73, 2, 3, 24, 20, 4, 10, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.31e+11 1.76e+11 1565197. 4.45e+10 2.31e+6 1.63e+11 1.84e+6 3.70e+4
## 2 after 2.08e+11 8.51e+10 1309752. 2.15e+10 2.04e+6 7.87e+10 1.56e+6 2.93e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.15e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 36,567
## Columns: 9
## $ date <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
##
## Integrated per capita data file:
## Rows: 53,457
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
saveToRDS(cdc_daily_221202_v2, ovrWriteError=FALSE)The components are compared:
# Raw data
all.equal(cdc_daily_221202$dfRaw$cdcWeeklyBurden, cdc_daily_221202_v2$dfRaw$cdcWeeklyBurden)## [1] TRUEall.equal(cdc_daily_221202$dfRaw$cdcHosp, cdc_daily_221202_v2$dfRaw$cdcHosp)## [1] TRUEall.equal(cdc_daily_221202$dfRaw$vax, cdc_daily_221202_v2$dfRaw$vax)## [1] TRUE# Processed data, other than vaccines
all.equal(cdc_daily_221202$dfProcess$cdcWeeklyBurden, cdc_daily_221202_v2$dfProcess$cdcWeeklyBurden)## [1] TRUEall.equal(cdc_daily_221202$dfProcess$cdcHosp, cdc_daily_221202_v2$dfProcess$cdcHosp)## [1] TRUE# Per capita data other than vaccines
identical(dim(cdc_daily_221202$dfPerCapita), dim(cdc_daily_221202_v2$dfPerCapita))## [1] TRUEidentical(names(cdc_daily_221202$dfPerCapita), names(cdc_daily_221202_v2$dfPerCapita))## [1] TRUEidentical(cdc_daily_221202$dfPerCapita %>% select(-contains("vx")),
cdc_daily_221202_v2$dfPerCapita %>% select(-contains("vx"))
)## [1] TRUE# Per-capita vaccines data plotted nationally
dfVaxTest <- cdc_daily_221202$dfPerCapita %>%
select(date, state, contains("vx")) %>%
bind_rows(cdc_daily_221202_v2$dfPerCapita %>% select(date, state, contains("vx")), .id="src") %>%
left_join(getStateData(), by="state") %>%
mutate(src=c("1"="Original", "2"="Modified")[src])
glimpse(dfVaxTest)## Rows: 106,914
## Columns: 16
## $ src <chr> "Original", "Original", "Original", "Original", "Original"…
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ name <chr> "Alabama", "Hawaii", "Indiana", "Louisiana", "Minnesota", …
## $ pop <dbl> 4903185, 1415872, 6732219, 4648794, 5639632, 1068778, 1048…# Plots for vxa, vxc, vxcgte65, vxcgte18
dfVaxTest %>%
select(src, date, vxa, vxc, vxcgte65, vxcgte18) %>%
group_by(src, date) %>%
summarize(across(where(is.numeric), specNA(sum)), .groups="drop") %>%
pivot_longer(-c(src, date)) %>%
filter(!is.na(value)) %>%
ggplot(aes(x=date, y=value/1000000)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name, scales="free_y") +
labs(title="National vaccine metrics after converting to daily", x=NULL, y="Number vaccinated (millions)")
# Plots for vxcpoppct, vxcgte65pct, vxcgte18pct
dfVaxTest %>%
select(src, date, pop, vxcpoppct, vxcgte65pct, vxcgte18pct) %>%
group_by(src, date) %>%
summarize(across(starts_with("vxc"),
.fns=function(x) specNA(sum)(x*pop)/sum(pop)
),
.groups="drop"
) %>%
pivot_longer(-c(src, date)) %>%
filter(!is.na(value)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name, scales="free_y") +
labs(title="National percent vaccinated metrics after converting to daily", x=NULL, y="% vaccinated")
# Plots for vxapm, vxcpm, vxapm7, vxcpm7
dfVaxTest %>%
select(src, date, pop, vxapm, vxcpm, vxapm7, vxcpm7) %>%
group_by(src, date) %>%
summarize(across(starts_with("vx"),
.fns=function(x) specNA(sum)(x*pop)/sum(pop)
),
.groups="drop"
) %>%
pivot_longer(-c(src, date)) %>%
filter(!is.na(value)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name, scales="free_y") +
labs(title="National per-million vaccine metrics after converting to daily", x=NULL, y="Vaccinated per million")
The update repairs the vaccinated per million rolling-7 totals while keeping close alignment for the other vaccine metrics. Processed data files are checked for proper periodicity of the data:
tempCheckCycle <- function(df, returnDF=FALSE) {
df <- df %>%
pivot_longer(-c(state, date)) %>%
filter(!is.na(value)) %>%
arrange(state, name, date) %>%
group_by(state, name) %>%
summarize(start=min(date),
end=max(date),
maxDelta=max(date-lag(date), na.rm=TRUE),
perDate=median(date-lag(date), na.rm=TRUE),
.groups="drop"
)
df %>% count(start, end, maxDelta, perDate, sort=TRUE) %>% print()
if(isTRUE(returnDF)) return(df)
}
# Updated file
tempCheckCycle(cdc_daily_221202_v2$dfProcess$cdcWeeklyBurden)## # A tibble: 1 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-01-22 2022-11-30 1 days 1 days 204tempCheckCycle(cdc_daily_221202_v2$dfProcess$cdcHosp)## # A tibble: 43 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-07-15 2022-12-01 1 days 1 days 79
## 2 2020-03-01 2022-12-01 1 days 1 days 14
## 3 2020-01-01 2022-12-01 1 days 1 days 6
## 4 2020-03-02 2022-12-01 1 days 1 days 4
## 5 2020-03-27 2022-12-01 1 days 1 days 3
## 6 2020-03-14 2022-12-01 1 days 1 days 2
## 7 2020-03-26 2022-12-01 7 days 1 days 2
## 8 2020-03-28 2022-12-01 1 days 1 days 2
## 9 2020-04-11 2022-12-01 6 days 1 days 2
## 10 2020-05-18 2022-12-01 14 days 1 days 2
## # … with 33 more rowstempCheckCycle(cdc_daily_221202_v2$dfProcess$vax)## # A tibble: 1 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-12-14 2022-11-30 1 days 1 days 357# Previous file
tempCheckCycle(cdc_daily_221202$dfProcess$vax)## # A tibble: 1 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-12-14 2022-11-30 7 days 1 days 357Processed vaccines data have been appropriately updated from a mix of weekly and daily to daily. The hospital data are further explored for potential discontinuities:
# Temporary data file
tempHosp <- tempCheckCycle(cdc_daily_221202_v2$dfProcess$cdcHosp, returnDF=TRUE)## # A tibble: 43 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-07-15 2022-12-01 1 days 1 days 79
## 2 2020-03-01 2022-12-01 1 days 1 days 14
## 3 2020-01-01 2022-12-01 1 days 1 days 6
## 4 2020-03-02 2022-12-01 1 days 1 days 4
## 5 2020-03-27 2022-12-01 1 days 1 days 3
## 6 2020-03-14 2022-12-01 1 days 1 days 2
## 7 2020-03-26 2022-12-01 7 days 1 days 2
## 8 2020-03-28 2022-12-01 1 days 1 days 2
## 9 2020-04-11 2022-12-01 6 days 1 days 2
## 10 2020-05-18 2022-12-01 14 days 1 days 2
## # … with 33 more rows# Plot of starting and ending dates
tempHosp %>%
select(state, name, start, end) %>%
pivot_longer(-c(state, name), names_to="time") %>%
ggplot(aes(x=fct_reorder(state, value, min), y=value)) +
geom_point(aes(color=time)) +
coord_flip() +
labs(title="Start and end dates for hospital metrics", x=NULL, y="Start and end date") +
facet_wrap(~name)
# Counts of most common periodicity
tempHosp %>%
count(name, perDate)## # A tibble: 3 × 3
## name perDate n
## <chr> <drtn> <int>
## 1 hosp_adult 1 days 51
## 2 hosp_ped 1 days 51
## 3 inp 1 days 51# Counts of maximum gap
tempHosp %>%
count(name, maxDelta)## # A tibble: 24 × 3
## name maxDelta n
## <chr> <drtn> <int>
## 1 hosp_adult 1 days 42
## 2 hosp_adult 2 days 1
## 3 hosp_adult 5 days 1
## 4 hosp_adult 6 days 1
## 5 hosp_adult 7 days 1
## 6 hosp_adult 10 days 1
## 7 hosp_adult 12 days 1
## 8 hosp_adult 14 days 1
## 9 hosp_adult 21 days 1
## 10 hosp_adult 84 days 1
## # … with 14 more rows# Examples with maximum gap greater than 1 day
tempHosp %>%
filter(maxDelta > 1) %>%
arrange(name, desc(maxDelta), state) %>%
print(n=100)## # A tibble: 22 × 6
## state name start end maxDelta perDate
## <chr> <chr> <date> <date> <drtn> <drtn>
## 1 NV hosp_adult 2020-01-02 2022-12-01 84 days 1 days
## 2 MI hosp_adult 2020-06-11 2022-12-01 21 days 1 days
## 3 MS hosp_adult 2020-05-18 2022-12-01 14 days 1 days
## 4 MA hosp_adult 2020-07-02 2022-12-01 12 days 1 days
## 5 CA hosp_adult 2020-04-03 2022-12-01 10 days 1 days
## 6 RI hosp_adult 2020-03-26 2022-12-01 7 days 1 days
## 7 NY hosp_adult 2020-04-11 2022-12-01 6 days 1 days
## 8 IA hosp_adult 2020-04-16 2022-12-01 5 days 1 days
## 9 LA hosp_adult 2020-04-06 2022-12-01 2 days 1 days
## 10 NV hosp_ped 2020-01-02 2022-12-01 191 days 1 days
## 11 MI hosp_ped 2020-06-11 2022-12-01 21 days 1 days
## 12 MS hosp_ped 2020-05-18 2022-12-01 14 days 1 days
## 13 MA hosp_ped 2020-07-02 2022-12-01 13 days 1 days
## 14 RI hosp_ped 2020-03-26 2022-12-01 7 days 1 days
## 15 NY hosp_ped 2020-04-11 2022-12-01 6 days 1 days
## 16 CA inp 2020-02-04 2022-12-01 22 days 1 days
## 17 NV inp 2020-01-02 2022-12-01 18 days 1 days
## 18 WV inp 2020-01-28 2022-12-01 16 days 1 days
## 19 UT inp 2020-03-14 2022-12-01 7 days 1 days
## 20 CO inp 2020-03-11 2022-12-01 3 days 1 days
## 21 VT inp 2020-03-17 2022-12-01 3 days 1 days
## 22 MS inp 2020-02-18 2022-12-01 2 days 1 days# Confirmation that gaps are from early 2020
tempCheckCycle(cdc_daily_221202_v2$dfProcess$cdcHosp %>% filter(date >= "2020-03-01"), returnDF=TRUE) %>%
filter(maxDelta > 1)## # A tibble: 32 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-07-15 2022-12-01 1 days 1 days 80
## 2 2020-03-01 2022-12-01 1 days 1 days 29
## 3 2020-03-02 2022-12-01 1 days 1 days 4
## 4 2020-03-27 2022-12-01 1 days 1 days 3
## 5 2020-03-14 2022-12-01 1 days 1 days 2
## 6 2020-03-26 2022-12-01 7 days 1 days 2
## 7 2020-03-28 2022-12-01 1 days 1 days 2
## 8 2020-04-11 2022-12-01 6 days 1 days 2
## 9 2020-05-18 2022-12-01 14 days 1 days 2
## 10 2020-06-11 2022-12-01 21 days 1 days 2
## # … with 22 more rows## # A tibble: 16 × 6
## state name start end maxDelta perDate
## <chr> <chr> <date> <date> <drtn> <drtn>
## 1 CA hosp_adult 2020-04-03 2022-12-01 10 days 1 days
## 2 CO inp 2020-03-11 2022-12-01 3 days 1 days
## 3 IA hosp_adult 2020-04-16 2022-12-01 5 days 1 days
## 4 LA hosp_adult 2020-04-06 2022-12-01 2 days 1 days
## 5 MA hosp_adult 2020-07-02 2022-12-01 12 days 1 days
## 6 MA hosp_ped 2020-07-02 2022-12-01 13 days 1 days
## 7 MI hosp_adult 2020-06-11 2022-12-01 21 days 1 days
## 8 MI hosp_ped 2020-06-11 2022-12-01 21 days 1 days
## 9 MS hosp_adult 2020-05-18 2022-12-01 14 days 1 days
## 10 MS hosp_ped 2020-05-18 2022-12-01 14 days 1 days
## 11 NY hosp_adult 2020-04-11 2022-12-01 6 days 1 days
## 12 NY hosp_ped 2020-04-11 2022-12-01 6 days 1 days
## 13 RI hosp_adult 2020-03-26 2022-12-01 7 days 1 days
## 14 RI hosp_ped 2020-03-26 2022-12-01 7 days 1 days
## 15 UT inp 2020-03-14 2022-12-01 7 days 1 days
## 16 VT inp 2020-03-17 2022-12-01 3 days 1 daystempCheckCycle(cdc_daily_221202_v2$dfProcess$cdcHosp %>% filter(date >= "2020-07-01"))## # A tibble: 8 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-07-15 2022-12-01 1 days 1 days 80
## 2 2020-07-01 2022-12-01 1 days 1 days 63
## 3 2020-07-01 2022-12-01 2 days 1 days 2
## 4 2020-07-06 2022-12-01 5 days 1 days 2
## 5 2020-07-11 2022-12-01 1 days 1 days 2
## 6 2020-07-28 2022-12-01 1 days 1 days 2
## 7 2020-07-02 2022-12-01 12 days 1 days 1
## 8 2020-07-02 2022-12-01 13 days 1 days 1tempCheckCycle(cdc_daily_221202_v2$dfProcess$cdcHosp %>% filter(date >= "2020-08-01"))## # A tibble: 1 × 5
## start end maxDelta perDate n
## <date> <date> <drtn> <drtn> <int>
## 1 2020-08-01 2022-12-01 1 days 1 days 153Hospital data were uneven in reporting ramp up, and appear to be consistently daily since August 2020.
The latest data are downloaded and processed:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230102.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_230102.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_230102.csv"
)
compareList <- list("cdcWeeklyBurden"=readFromRDS("cdc_daily_221202_v2")$dfRaw$cdcWeeklyBurden,
"cdcHosp"=readFromRDS("cdc_daily_221202_v2")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_221202_v2")$dfRaw$vax
)
cdc_daily_230102 <- readRunCDCDaily(thruLabel="Dec 31, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 9240 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-03-18 tot_deaths 141 150 9 0.06185567
## 2 2020-04-01 tot_deaths 6360 6741 381 0.05816350
## 3 2021-12-08 new_deaths 8859 11250 2391 0.23780397
## 4 2020-04-01 new_deaths 5092 5437 345 0.06553329
## 5 2020-03-18 new_deaths 127 135 8 0.06106870
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 CO tot_deaths 1058420 1085292 26872 0.025070532
## 2 ND tot_deaths 206155 202764 3391 0.016585192
## 3 KS tot_deaths 724691 722294 2397 0.003313096
## 4 VT tot_deaths 48925 48976 51 0.001041869
## 5 CO tot_cases 106257226 107496419 1239193 0.011594591
## 6 VT tot_cases 7690693 7742157 51464 0.006669410
## 7 KS tot_cases 59319698 59401024 81326 0.001370039
## 8 MO new_deaths 19594 22038 2444 0.117409685
## 9 ND new_deaths 2410 2232 178 0.076691081
## 10 CO new_deaths 13650 13609 41 0.003008181
## 11 VT new_deaths 772 770 2 0.002594034
## 12 WA new_deaths 14705 14743 38 0.002580820
## 13 IN new_deaths 25101 25164 63 0.002506714
## 14 KY new_cases 1639275 1635283 3992 0.002438192
## 15 CO new_cases 1705255 1708264 3009 0.001762990
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 9,240
## Columns: 10
## $ date_updated <date> 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-13, 2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <date> 2020-01-16, 2020-01-23, 2020-01-30, 2020-02-06, 2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…## Rows: 55685 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 6791 5318 1473 0.2432901
## 2 2020-07-25 hosp_ped 4374 5434 1060 0.2161501
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AS inp 754 753 1 0.001327140
## 2 WA inp 769395 768601 794 0.001032512
## 3 NH hosp_ped 1392 1435 43 0.030420941
## 4 KY hosp_ped 24970 24715 255 0.010264667
## 5 AR hosp_ped 14668 14525 143 0.009796869
## 6 VA hosp_ped 21330 21538 208 0.009704208
## 7 NV hosp_ped 6377 6430 53 0.008276724
## 8 KS hosp_ped 5637 5596 41 0.007299920
## 9 AL hosp_ped 23270 23116 154 0.006639934
## 10 ID hosp_ped 4725 4695 30 0.006369427
## 11 WV hosp_ped 6770 6809 39 0.005744164
## 12 DE hosp_ped 6201 6236 35 0.005628367
## 13 TN hosp_ped 24598 24473 125 0.005094659
## 14 MS hosp_ped 13755 13687 68 0.004955907
## 15 NM hosp_ped 9383 9429 46 0.004890495
## 16 NJ hosp_ped 22736 22626 110 0.004849874
## 17 IN hosp_ped 19521 19433 88 0.004518150
## 18 MA hosp_ped 14977 15036 59 0.003931630
## 19 SD hosp_ped 4786 4799 13 0.002712572
## 20 SC hosp_ped 10316 10294 22 0.002134886
## 21 MD hosp_ped 22296 22340 44 0.001971503
## 22 PR hosp_ped 26254 26305 51 0.001940676
## 23 CO hosp_ped 24913 24876 37 0.001486272
## 24 IA hosp_ped 8897 8910 13 0.001460100
## 25 MO hosp_ped 45768 45832 64 0.001397380
## 26 VT hosp_ped 766 767 1 0.001304631
## 27 OK hosp_ped 29459 29493 34 0.001153481
## 28 NC hosp_ped 35349 35385 36 0.001017898
## 29 AS hosp_adult 728 727 1 0.001374570
## 30 WA hosp_adult 693214 692426 788 0.001137381
##
##
##
## Raw file for cdcHosp:
## Rows: 55,685
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 37272 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (101): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
## lgl (6): Second_Booster, Administered_Bivalent, Admin_Bivalent_PFR, Admin_...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 37,272
## Columns: 109
## $ date <date> 2022-12-28, 2022-12-28, 2022-1…
## $ MMWR_week <dbl> 52, 52, 52, 52, 52, 52, 52, 52,…
## $ state <chr> "AS", "PA", "WA", "CO", "ID", "…
## $ Distributed <dbl> 128480, 40583485, 24157865, 168…
## $ Distributed_Janssen <dbl> 600, 1564300, 776000, 500700, 1…
## $ Distributed_Moderna <dbl> 25500, 15149120, 8579700, 59393…
## $ Distributed_Pfizer <dbl> 102380, 23816365, 14772865, 104…
## $ Distributed_Novavax <dbl> 0, 53700, 29300, 22000, 15200, …
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 271101, 317009, 317245, 292906,…
## $ Distributed_Per_100k_5Plus <dbl> 298028, 335288, 337475, 310837,…
## $ Distributed_Per_100k_12Plus <dbl> 348024, 366226, 371607, 341724,…
## $ Distributed_Per_100k_18Plus <dbl> 410021, 399154, 405890, 374903,…
## $ Distributed_Per_100k_65Plus <dbl> 3919460, 1695670, 1996980, 2002…
## $ vxa <dbl> 114961, 26925901, 17293339, 127…
## $ Administered_5Plus <dbl> 114942, 26742703, 17141828, 126…
## $ Administered_12Plus <dbl> 102201, 25829778, 16455287, 121…
## $ Administered_18Plus <dbl> 85505, 24381114, 15431853, 1140…
## $ Administered_65Plus <dbl> 9429, 8262432, 4441433, 3107644…
## $ Administered_Janssen <dbl> 580, 797890, 494053, 346033, 90…
## $ Administered_Moderna <dbl> 25530, 10321238, 6348861, 48039…
## $ Administered_Pfizer <dbl> 87577, 15802958, 10429663, 7593…
## $ Administered_Novavax <dbl> 0, 1869, 2910, 2351, 420, 7, 21…
## $ Administered_Unk_Manuf <dbl> 1274, 1946, 17852, 18929, 5225,…
## $ Admin_Per_100k <dbl> 242575, 210326, 227099, 221665,…
## $ Admin_Per_100k_5Plus <dbl> 266625, 220940, 239464, 233235,…
## $ Admin_Per_100k_12Plus <dbl> 276840, 233089, 253122, 246620,…
## $ Admin_Per_100k_18Plus <dbl> 272874, 239798, 259279, 253536,…
## $ Admin_Per_100k_65Plus <dbl> 287645, 345223, 367145, 368898,…
## $ Recip_Administered <dbl> 115406, 26989394, 17496405, 127…
## $ Administered_Dose1_Recip <dbl> 46141, 11538334, 6462376, 47993…
## $ Administered_Dose1_Pop_Pct <dbl> 95.0, 90.1, 84.9, 83.3, 63.7, 6…
## $ Administered_Dose1_Recip_5Plus <dbl> 46123, 11446764, 6386218, 47462…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 95.0, 94.6, 89.2, 87.5, 67.8, 7…
## $ Administered_Dose1_Recip_12Plus <dbl> 39545, 11004559, 6085491, 45229…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 95.0, 93.6, 91.6, 73.2, 8…
## $ Administered_Dose1_Recip_18Plus <dbl> 32760, 10331728, 5660324, 41955…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 93.2, 76.4, 8…
## $ Administered_Dose1_Recip_65Plus <dbl> 3307, 2892379, 1292821, 899292,…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9…
## $ vxc <dbl> 42436, 9338897, 5766796, 421754…
## $ vxcpoppct <dbl> 89.5, 72.9, 75.7, 73.2, 56.3, 5…
## $ Series_Complete_5Plus <dbl> 42436, 9281594, 5721938, 419161…
## $ Series_Complete_5PlusPop_Pct <dbl> 95.0, 76.7, 79.9, 77.2, 60.1, 5…
## $ Series_Complete_12Plus <dbl> 36363, 8920423, 5456376, 400392…
## $ Series_Complete_12PlusPop_Pct <dbl> 95.0, 80.5, 83.9, 81.1, 64.9, 6…
## $ vxcgte18 <dbl> 29937, 8371701, 5068959, 371122…
## $ vxcgte18pct <dbl> 95.0, 82.3, 85.2, 82.5, 67.6, 6…
## $ vxcgte65 <dbl> 2993, 2423187, 1183842, 815304,…
## $ vxcgte65pct <dbl> 91.3, 95.0, 95.0, 95.0, 90.3, 7…
## $ Series_Complete_Janssen <dbl> 585, 739294, 459368, 316985, 83…
## $ Series_Complete_Moderna <dbl> 9540, 3339802, 1978469, 1496432…
## $ Series_Complete_Pfizer <dbl> 31680, 5241387, 3306331, 238680…
## $ Series_Complete_Novavax <dbl> 0, 679, 921, 781, 197, 0, 716, …
## $ Series_Complete_Unk_Manuf <dbl> 631, 878, 6443, 5149, 1110, 11,…
## $ Series_Complete_Janssen_5Plus <dbl> 585, 739237, 459338, 316965, 83…
## $ Series_Complete_Moderna_5Plus <dbl> 9540, 3300797, 1949285, 1482341…
## $ Series_Complete_Pfizer_5Plus <dbl> 31680, 5240009, 3305982, 238641…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 631, 872, 6412, 5116, 1110, 11,…
## $ Series_Complete_Janssen_12Plus <dbl> 585, 739177, 459310, 316955, 83…
## $ Series_Complete_Moderna_12Plus <dbl> 9539, 3297951, 1947465, 1481220…
## $ Series_Complete_Pfizer_12Plus <dbl> 25609, 4881749, 3042481, 220005…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 630, 867, 6200, 4914, 1099, 11,…
## $ Series_Complete_Janssen_18Plus <dbl> 584, 738607, 458936, 316606, 83…
## $ Series_Complete_Moderna_18Plus <dbl> 9516, 3294508, 1946193, 1480227…
## $ Series_Complete_Pfizer_18Plus <dbl> 19211, 4337132, 2657093, 190955…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 626, 801, 5843, 4086, 1086, 11,…
## $ Series_Complete_Janssen_65Plus <dbl> 28, 93099, 59525, 29261, 12413,…
## $ Series_Complete_Moderna_65Plus <dbl> 1217, 1131699, 532195, 386898, …
## $ Series_Complete_Pfizer_65Plus <dbl> 1731, 1197976, 589701, 397808, …
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 17, 323, 2295, 1226, 387, 4, 40…
## $ Additional_Doses <dbl> 24639, 4388233, 3334585, 241392…
## $ Additional_Doses_Vax_Pct <dbl> 58.1, 47.0, 57.8, 57.2, 48.3, 3…
## $ Additional_Doses_5Plus <dbl> 24639, 4387862, 3334410, 241371…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 58.1, 47.3, 58.3, 57.6, 48.4, 3…
## $ Additional_Doses_12Plus <dbl> 24610, 4310217, 3240993, 235688…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 67.7, 48.3, 59.4, 58.9, 49.3, 3…
## $ Additional_Doses_18Plus <dbl> 21165, 4136928, 3075799, 223670…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 70.7, 49.4, 60.7, 60.3, 50.8, 3…
## $ Additional_Doses_50Plus <dbl> 8420, 2837491, 1764972, 1251228…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 79.5, 60.4, 73.1, 73.3, 64.5, 4…
## $ Additional_Doses_65Plus <dbl> 2408, 1655853, 958848, 657580, …
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 80.5, 68.3, 81.0, 80.7, 72.6, 4…
## $ Additional_Doses_Moderna <dbl> 4942, 1844008, 1360084, 1007064…
## $ Additional_Doses_Pfizer <dbl> 19691, 2480159, 1932960, 137743…
## $ Additional_Doses_Janssen <dbl> 2, 63840, 39351, 27171, 6578, 1…
## $ Additional_Doses_Unk_Manuf <dbl> 4, 191, 2010, 2131, 243, 3, 282…
## $ Second_Booster <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 2187, 1356637, 1100696, 762354,…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 26.0, 47.8, 62.4, 60.9, 52.5, 1…
## $ Second_Booster_65Plus <dbl> 847, 910537, 671524, 453730, 11…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 35.2, 55.0, 70.0, 69.0, 59.3, 1…
## $ Second_Booster_Janssen <dbl> 0, 490, 765, 438, 97, 1, 1223, …
## $ Second_Booster_Moderna <dbl> 515, 697069, 615503, 401490, 83…
## $ Second_Booster_Pfizer <dbl> 1813, 972835, 982330, 674052, 1…
## $ Second_Booster_Unk_Manuf <dbl> 0, 55, 751, 1000, 88, 0, 1520, …
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Bivalent_Booster_5Plus <dbl> 383, 2053363, 1677721, 1098349,…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 0.9, 17.0, 23.4, 20.2, 12.9, 2.…
## $ Bivalent_Booster_12Plus <dbl> 380, 2018307, 1627350, 1072392,…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 1.0, 18.2, 25.0, 21.7, 14.2, 3.…
## $ Bivalent_Booster_18Plus <dbl> 377, 1957486, 1564680, 1030800,…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 1.2, 19.3, 26.3, 22.9, 15.4, 3.…
## $ Bivalent_Booster_65Plus <dbl> 146, 967521, 630616, 415661, 10…
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 4.5, 40.4, 52.1, 49.3, 37.7, 8.…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 6.68e+9 9.12e+7 1.01e+8 1084729 9086
## 2 after 6.63e+9 9.08e+7 9.94e+7 1078513 7854
## 3 pctchg 7.94e-3 4.79e-3 1.19e-2 0.00573 0.136
##
##
## Processed for cdcWeeklyBurden:
## Rows: 54,672
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.49e+7 4.81e+7 1403673 55685
## 2 after 5.46e+7 4.78e+7 1376627 53148
## 3 pctchg 5.43e-3 5.19e-3 0.0193 0.0456
##
##
## Processed for cdcHosp:
## Rows: 53,148
## Columns: 5
## $ date <date> 2021-01-26, 2021-01-20, 2021-01-20, 2021-01-17, 2021-01-13…
## $ state <chr> "LA", "MA", "SD", "MT", "CT", "ND", "NV", "RI", "MN", "OR",…
## $ inp <dbl> 1502, 2084, 189, 181, 1274, 162, 1817, 449, 784, 560, 3520,…
## $ hosp_adult <dbl> 1488, 2056, 189, 178, 1267, 159, 1810, 446, 766, 551, 3475,…
## $ hosp_ped <dbl> 14, 28, 0, 3, 7, 3, 7, 3, 18, 10, 45, 5, 4, 4, 2, 11, 81, 1…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.36e+11 1.78e+11 1581868. 4.49e+10 2.33e+6 1.64e+11 1.86e+6 3.73e+4
## 2 after 2.10e+11 8.60e+10 1323572. 2.17e+10 2.06e+6 7.94e+10 1.57e+6 2.95e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.15e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 37,995
## Columns: 9
## $ date <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
##
## Integrated per capita data file:
## Rows: 55,038
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
saveToRDS(cdc_daily_230102, ovrWriteError=FALSE)The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20230103 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20230103.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20230103.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 229552 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 229,552
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 4268
## 2 Critical Access Hospitals 61538
## 3 Long Term 15479
## 4 Short Term 148267
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 24
## 2 GU 96
## 3 MP 39
## 4 PR 2260
## 5 VI 83
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 62461 166729 362 229552
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 28781 184071 16700 229552
## 3 icu_beds_used_7_day_avg 31700 173520 24332 229552
## 4 inpatient_beds_7_day_avg 7838 220855 859 229552
## 5 inpatient_beds_used_7_day_avg 7838 203226 18488 229552
## 6 inpatient_beds_used_covid_7_day_avg 1684 153682 74186 229552
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 28738 136723 64091 229552
## 8 total_adult_patients_hospitalized_confirmed_and… 26324 137598 65630 229552
## 9 total_beds_7_day_avg 39577 189761 214 229552
## 10 total_icu_beds_7_day_avg 3195 214765 11592 229552
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20230103, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_230102 <- postProcessCDCDaily(cdc_daily_230102,
dataThruLabel="Dec 2022",
keyDatesBurden=c("2022-12-31", "2022-06-30",
"2021-12-31", "2021-06-30"
),
keyDatesVaccine=c("2022-12-28", "2021-12-31",
"2021-08-31", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 9 rows containing missing values (`geom_line()`).
## Warning: Removed 51 rows containing missing values (`geom_col()`).## Warning: Removed 51 rows containing missing values (`geom_text()`).## Warning: Removed 51 rows containing missing values (`geom_col()`).## Warning: Removed 51 rows containing missing values (`geom_text()`).
# Create hospitalized per capita data
hospPerCap_230102 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_230102,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 rows containing missing values (`geom_line()`).
burdenPivotList_230102$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.96e4 55685
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 3.06e5 55685
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.41e5 55685
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.22e5 55685
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.37e5 55685
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.12e6 55685
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.15e6 55685
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 1.07e6 55685
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.22e4 55685
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.81e5 55685
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.69e5 55685
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.73e5 55685
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.91e5 55685
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 8.24e5 55685
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 8.07e5 55685
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 7.38e5 55685
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.90e5 55685
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.40e5 55685saveToRDS(burdenPivotList_230102, ovrWriteError=FALSE)
saveToRDS(hospPerCap_230102, ovrWriteError=FALSE)Peaks and valleys are also updated, once using the existing process, and once using the updated process:
# Existing process
peakValleyCDCDaily(cdc_daily_230102)## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## # A tibble: 9,144 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 9,134 more rows# Updated process
dfPeaks_230102 <- cdc_daily_230102$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
updatedMakePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"=paste0("Rolling ", 7, "-day mean deaths"),
"new_cases"=paste0("Rolling ", 7, "-day mean cases (000)"),
"inp"=paste0("Rolling ", 7, "-day mean in hospital (000)")
)
)## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
dfPeaksVax_230102 <- cdc_daily_230102$dfPerCapita %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(c("state", "date", "vxc", "vxa")) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
filter(date >= as.Date("2020-12-15")) %>%
updatedMakePeakValley(numVar=c("vxa", "vxc"),
windowWidth = 71,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"=paste0("Vaccines adminsitered (states with population >= ", 8000000, ")"),
"vxc"=paste0("Became fully vaccinated (states with population >= ", 8000000, ")")
),
yLab=c("vxa"=paste0("Rolling ", 21, "-day mean administered (000)"),
"vxc"=paste0("Rolling ", 21,"-day mean completed (000)")
)
)## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
Hospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20230102 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20230103")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20230102 <- hospitalCapacityCDCDaily(multiSourceHosp_20230102,
plotSub="Aug 2020 to Dec 2022\nOld data used pre-2022"
)
